diff --git a/README.md b/README.md index 4b32544..1b4a6fe 100644 --- a/README.md +++ b/README.md @@ -28,7 +28,8 @@ cp .env.example .env # set WORKER to the other Spark's ssh destination Measured on two DGX Sparks joined by one 200G cable, with images built from a fresh clone. Receipts and caveats: -[this release: KV shard, deterministic verify, faster MoE decode](results/2026-10-09-rc2/RESULT.md), +[this release: faster single-stream decode, bit-exact with RC2](results/2026-10-12-rc4/RESULT.md), +[RC2: KV shard, deterministic verify, faster MoE decode](results/2026-10-09-rc2/RESULT.md), [the display carveout's KV placement](results/2026-10-05-carveout-placement/RESULT.md), [a faster verify step and cheaper warm turns](results/2026-09-30-decode-step/RESULT.md), [RigMark decode, prefill and staggered arrivals on 2026-10-05](results/2026-10-05-rigmark/RESULT.md), @@ -50,14 +51,14 @@ and [the first Atlas release](results/2026-09-29-atlas-merged/RESULT.md) | New session sharing a 24K system prompt with earlier ones: time to first token | **15.7 s** | 36.2 s with the cache policy off | | Idle session resuming after another session's 17 turns (median) | **2.5 s** | 49.6 s with the cache policy off | | Cold 61K / 125K prompt: time to first token | **25.0 / 49.1 s** | 29.2 / 62.5 s two engines ago | -| Matrix: C1/C2 at 16K and 32K, C4 at 16K, 400 tokens each, cold (sum of walls) | **136.3 s** | vLLM SparkGLM 206.6 s · Mia TensorFold v1.10 166.9 s | -| Staggered C4: four ~16K requests arriving 1 s apart, cold (median of 5) | **49.9 s** | vLLM SparkGLM 70.5 s · Mia TensorFold v1.10 60.2 s · Mia EXL3 113.2 s | -| Staggered C4: four ~32K requests arriving 1 s apart, cold (median of 3) | **77.3 s** | Mia TensorFold v1.10 100.1 s | -| Single-stream decode, 12 prose / 12 code prompts × 256 tokens | **42.9 / 61.7 tok/s** | 41.2 / 61.8 on 2026-10-09 morning; Mia TensorFold v1.10 is faster here ([release notes](results/2026-10-09-rc2/RESULT.md)) | -| RigMark decode, code / prose / structured (thinking on, low effort) | **65.5 / 36.5 / 88.8 tok/s** | [RiNGSiDE](https://github.com/othexmr/GLM-5.3-Flash-NVFP4-2x-4x-DGX-Sparks-RiNGSiDE) vLLM TP2 (published) 56.5 / 33.0 / 83.4 | -| RigMark cold prefill 8K / 32K / 64K: time to first token | **3.29 / 12.47 / 23.72 s** | RiNGSiDE 3.56 / 12.94 / 25.66 s | +| Matrix: C1/C2 at 16K and 32K, C4 at 16K, 400 tokens each, cold (sum of walls) | **132.1 s** | vLLM SparkGLM 206.6 s · Mia TensorFold v1.10 166.9 s | +| Staggered C4: four ~16K requests arriving 1 s apart, cold (median of 5) | **49.5 s** | vLLM SparkGLM 70.5 s · Mia TensorFold v1.10 60.2 s · Mia EXL3 113.2 s | +| Staggered C4: four ~32K requests arriving 1 s apart, cold (median of 3) | **76.3 s** | Mia TensorFold v1.10 100.1 s | +| Single-stream decode, 12 prose / 12 code prompts × 256 tokens | **45.6 / 64.9 tok/s** | 42.8 / 61.4 with RC2 (2026-10-09), same output bits ([release notes](results/2026-10-12-rc4/RESULT.md)) | +| RigMark decode, code / prose / structured (thinking on, low effort) | **72.3 / 38.3 / 91.7 tok/s** | [RiNGSiDE](https://github.com/othexmr/GLM-5.3-Flash-NVFP4-2x-4x-DGX-Sparks-RiNGSiDE) vLLM TP2 (published) 56.5 / 33.0 / 83.4 | +| RigMark cold prefill 8K / 32K / 64K: time to first token | **3.29 / 12.47 / 23.91 s** | RiNGSiDE 3.56 / 12.94 / 25.66 s | | RigMark staggered arrivals, prefill first: newcomer time to first token at 2 / 4 | **2.80 / 3.54 s** | RiNGSiDE 4.70 / 5.15 s | -| RigMark short code, 1 / 4 streams, aggregate | **47.7 / 69.5 tok/s** | RiNGSiDE 44.0 at 1 stream; 97.3 at 6 streams (our 4 × 1M profile serves 4 at a time) | +| RigMark short code, 1 / 4 streams, aggregate | **47.0 / 70.9 tok/s** | RiNGSiDE 44.0 at 1 stream; 97.3 at 6 streams (our 4 × 1M profile serves 4 at a time) | | Returning to a ~209K-token conversation evicted to disk (prefix cache on disk, 48 GB) | **1.2–1.5 s** | about 82 s without it | | Strict JSON (`response_format` json_schema), ~2,000-token structured answer | **30–50 s**, schema-valid | 135–155 s with masked serial decode | | Strict JSON object with 96 required keys, temperature 0 | **39 s**, 59 tok/s, valid | engine killed (out of memory) two releases ago | @@ -254,17 +255,15 @@ comparison videos. ## The engine -Atlas here is [`Enntity/atlas`](https://github.com/Enntity/atlas) commit -`f2b805e7` on `sparkglm/atlas-20261009-rc2` (pinned in -[`install/atlas-source.json`](install/atlas-source.json)). Its tree is -identical to `sparkglm/atlas-20261009-rc2-layered` at `fea1ef6c` (tree -`513bf0c7`), which is built from three +Atlas here is [`Enntity/atlas`](https://github.com/Enntity/atlas) branch +`sparkglm/atlas-20261012-rc4.1-layered` at `301479a2` (tree `ac5a080b`, pinned in +[`install/atlas-source.json`](install/atlas-source.json)), built from three layers: 1. Atlas-Inf `main` base at `6a3d24ec`. 2. GLM-5.3-Flash support and optimizations (`upstream/glm53-flash`, at this - release `upstream/glm53-flash-20261009-rc2`), which we intend to propose - to Atlas-Inf after review. It started as + release `upstream/glm53-flash` at `0b515eec` plus the tool-value fix + `ade7b012`), which we intend to propose to Atlas-Inf after review. It started as Reiner Schmidt's port ([Mango-kid/atlas](https://github.com/Mango-kid/atlas/tree/feat/glm53-dual-spark)); the engine's `docs/porting/GLM_5_3_FLASH.md` has the history. @@ -273,11 +272,9 @@ layers: display carveout (including where the split KV pools are placed in it). These are published Enntity release snapshots, separate from current -Atlas-Inf `main`. The manifest's top-level commit and tree identify the -measured engine; its `layers` entries record the earlier integration history. -The tree-equivalent layered reconstruction uses the RC2 contribution series -at `a1c9b1bc`. The manifest stays unchanged so the install tree still names -the measured image. +Atlas-Inf `main`. The pinned tree is RC4's measured tree (`exp/prose-rc4` at +`c6089633`) plus the GLM tool-value fix and four CI-only upstream commits; the +manifest's `layers` entries record the layering. ## History diff --git a/docs/INSTALL-MANUAL.md b/docs/INSTALL-MANUAL.md index 369cbb0..f89a5d0 100644 --- a/docs/INSTALL-MANUAL.md +++ b/docs/INSTALL-MANUAL.md @@ -5,7 +5,7 @@ time. Run steps 1–4 on **both** Sparks with the same `MODEL_ROOT`. | Component | Pin | |---|---| -| Engine | [`Enntity/atlas`](https://github.com/Enntity/atlas) `sparkglm/atlas-20261009-rc2` @ `f2b805e7` ([`install/atlas-source.json`](../install/atlas-source.json)) | +| Engine | [`Enntity/atlas`](https://github.com/Enntity/atlas) `sparkglm/atlas-20261012-rc4.1-layered` @ `301479a2` ([`install/atlas-source.json`](../install/atlas-source.json)) | | Model | [`nvidia/GLM-5.3-Flash-NVFP4`](https://huggingface.co/nvidia/GLM-5.3-Flash-NVFP4) @ `423acf37583782c51c142d145aef733d72943d93` (MIT) | | Drafter | [`incoai/GLM-5.3-Flash-DFlash2`](https://huggingface.co/incoai/GLM-5.3-Flash-DFlash2) @ `7d74cdd881ed7e32c31175984a67823127b66cfe` (CC BY-NC-ND 4.0) | | Native dependencies | FlashInfer `8eccd0c1`, CUTLASS `cf064d2e`, FlashKDA ([`install/flash_kda/`](../install/flash_kda/)), Rust 1.93.1, CUDA 13.0 | diff --git a/install/atlas-source.json b/install/atlas-source.json index 0bf77a6..510739d 100644 --- a/install/atlas-source.json +++ b/install/atlas-source.json @@ -2,24 +2,24 @@ "schema": "sparkglm.atlas-engine-source/v2", "note": "The Atlas engine is built from the public Enntity/atlas fork at this exact commit; build.sh refuses any other tree.", "repository": "https://github.com/Enntity/atlas.git", - "branch": "sparkglm/atlas-20261009-rc2", - "commit": "f2b805e73d36a7e5527622e24f2f8199052e01e6", - "tree": "513bf0c7187cc202a54980533fb4c5325ec72b2e", + "branch": "sparkglm/atlas-20261012-rc4.1-layered", + "commit": "301479a20612c91243266a3fc7eea3100b99ad59", + "tree": "ac5a080b608b6cbdf420db63d76906ea450392dd", "layers": { "atlas_inf_main_base": "6a3d24ecb891adac1fac8edeef92de29bb6f3e15", "upstream_candidate": { - "branch": "upstream/glm53-flash-20261009", - "commit": "576ad34e", - "note": "upstream/glm53-flash-20261007-strings (GLM tool arguments keep their schema types; no effort header on the empty think block) plus copy (prompt-lookup) drafts beside DFlash2 (ATLAS_DFLASH_COPY_DRAFTS), the bare role-literal strip limited to ChatML models (GLM streamed replies lost the words tool/user/assistant), and Poolside tool-call headers held until the call validates." + "branch": "fix/glm-tool-value-markers", + "commit": "ade7b01281b422d2fefb16205685aabc3d46d9f4", + "note": "upstream/glm53-flash at 0b515eec (upstream/glm53-flash-20261012-rc4: the RC4 engine commits on a1c9b1bc plus four CI-only fixes, none changing GB10 runtime behavior) plus the GLM tool-value fix: the envelope-stuck guard (MAX_TOOL_BODY_TOKENS = 1024) recognized parameter values only by Qwen's token ids, so it cut any GLM tool call longer than about 1024 tokens (finish_reason length, no tool call); GLM's single-token / are now resolved at startup and value bodies tracked by them." }, "sparkglm_only": { - "commit": "72588541a466f53cb9d38afd74c515e14ab601db", - "note": "The unchanged FlashKDA/native sparse-MLA prefill bridges and GB10 display-carveout KV placement from fdd2f965, then the carveout takes latent KV pools only (ATLAS_KV_CARVEOUT_ORDER, default latent): the GPU maps the carveout L2-uncached, and sparse-index buffers there slowed 64K cold prefill by 7% (Enntity/sparkglm#30). Fork-only; used only with DISPLAY_CARVEOUT=1. Plus: sharded latent pools (ATLAS_GLM_KV_SHARD, opt-in through SPARKGLM_KV_SHARD) size and place in the display carveout." + "commit": "301479a20612c91243266a3fc7eea3100b99ad59", + "note": "The seven SparkGLM-only commits re-layered on the upstream candidate: FlashKDA/native sparse-MLA prefill bridges (the first, 661a26a0, now declares the KV-shard flag its native bridge reads, which upstream dropped as unused), GB10 display-carveout KV placement (latent pools only, ATLAS_KV_CARVEOUT_ORDER), and sharded latent pools (ATLAS_GLM_KV_SHARD, opt-in through SPARKGLM_KV_SHARD) sized and placed in the carveout. Fork-only; the carveout is used only with DISPLAY_CARVEOUT=1." }, "integration": { - "branch": "sparkglm/atlas-20261009-rc2", - "commit": "b85befc6 + 6", - "note": "sparkglm/atlas-20261009-final plus: the canonical verify (exp/canonical-verify: one row-invariant kernel family per op for every verify width, plain decode as a one-row verify, dense FFN included, so greedy output no longer depends on the DFlash width, the drafter or concurrency; ATLAS_GLM_CANONICAL_VERIFY, default on), persistent prefetching MoE decode kernels (exp/moe-decode-bw, bit-identical; ATLAS_GLM_MOE_DECODE_PERSIST, on in the profiles) and one-launch KDA record commits (exp/kda-commit-batch, bit-identical), cherry-picked with -x." + "branch": "sparkglm/atlas-20261012-rc4-layered", + "commit": "5c523903c886d1abf2f4bda6a0af1cdf91c28523", + "note": "RC4 (tree ae5e79f8, the measured exp/prose-rc4 c6089633). This pin differs from it by the tool-value fix (spark-server only) and the four CI-only upstream commits; the decode path is unchanged." } }, "kernel_target": "glm-5.3-flash", diff --git a/install/profiles/4x1m.json b/install/profiles/4x1m.json index 3f49dcf..7fcf694 100644 --- a/install/profiles/4x1m.json +++ b/install/profiles/4x1m.json @@ -183,7 +183,18 @@ "ATLAS_USE_HF_REF_JSON_DUMPS": "1", "ATLAS_DFLASH_COPY_DRAFTS": "1", "ATLAS_DFLASH_COPY_REPLY_MATCH": "16", - "ATLAS_DFLASH_COPY_MISS_MAX": "3" + "ATLAS_DFLASH_COPY_MISS_MAX": "3", + "ATLAS_DFLASH_CONF_PROBE": "256", + "ATLAS_RDMA_ONESHOT_SM_STAGE": "1", + "ATLAS_GLM_MOE_ROUTE_FUSE": "1", + "ATLAS_GLM_DENSE_TP_SPLIT": "1", + "ATLAS_GLM_KDA_COMMIT_CTAS": "96", + "ATLAS_GLM_DRAFT_TP_TOPK": "1", + "ATLAS_DFLASH_ASYNC_UPLOADS": "1", + "ATLAS_GLM_MLA_FUSE": "1", + "ATLAS_GLM_MXFP8_TILED": "1", + "ATLAS_DFLASH2_EDGE": "0.6", + "ATLAS_DFLASH_CONV_TWIN": "nvfp4" }, "server_argv": [ "serve", diff --git a/install/profiles/4x512k.json b/install/profiles/4x512k.json index 2d0fd7e..a7ddb5d 100644 --- a/install/profiles/4x512k.json +++ b/install/profiles/4x512k.json @@ -183,7 +183,18 @@ "ATLAS_USE_HF_REF_JSON_DUMPS": "1", "ATLAS_DFLASH_COPY_DRAFTS": "1", "ATLAS_DFLASH_COPY_REPLY_MATCH": "16", - "ATLAS_DFLASH_COPY_MISS_MAX": "3" + "ATLAS_DFLASH_COPY_MISS_MAX": "3", + "ATLAS_DFLASH_CONF_PROBE": "256", + "ATLAS_RDMA_ONESHOT_SM_STAGE": "1", + "ATLAS_GLM_MOE_ROUTE_FUSE": "1", + "ATLAS_GLM_DENSE_TP_SPLIT": "1", + "ATLAS_GLM_KDA_COMMIT_CTAS": "96", + "ATLAS_GLM_DRAFT_TP_TOPK": "1", + "ATLAS_DFLASH_ASYNC_UPLOADS": "1", + "ATLAS_GLM_MLA_FUSE": "1", + "ATLAS_GLM_MXFP8_TILED": "1", + "ATLAS_DFLASH2_EDGE": "0.6", + "ATLAS_DFLASH_CONV_TWIN": "nvfp4" }, "server_argv": [ "serve", diff --git a/install/profiles/8x128k.json b/install/profiles/8x128k.json index 7a2c27c..8bfd950 100644 --- a/install/profiles/8x128k.json +++ b/install/profiles/8x128k.json @@ -182,7 +182,18 @@ "ATLAS_USE_HF_REF_JSON_DUMPS": "1", "ATLAS_DFLASH_COPY_DRAFTS": "1", "ATLAS_DFLASH_COPY_REPLY_MATCH": "16", - "ATLAS_DFLASH_COPY_MISS_MAX": "3" + "ATLAS_DFLASH_COPY_MISS_MAX": "3", + "ATLAS_DFLASH_CONF_PROBE": "256", + "ATLAS_RDMA_ONESHOT_SM_STAGE": "1", + "ATLAS_GLM_MOE_ROUTE_FUSE": "1", + "ATLAS_GLM_DENSE_TP_SPLIT": "1", + "ATLAS_GLM_KDA_COMMIT_CTAS": "96", + "ATLAS_GLM_DRAFT_TP_TOPK": "1", + "ATLAS_DFLASH_ASYNC_UPLOADS": "1", + "ATLAS_GLM_MLA_FUSE": "1", + "ATLAS_GLM_MXFP8_TILED": "1", + "ATLAS_DFLASH2_EDGE": "0.6", + "ATLAS_DFLASH_CONV_TWIN": "nvfp4" }, "server_argv": [ "serve", diff --git a/results/2026-10-12-rc4/RESULT.md b/results/2026-10-12-rc4/RESULT.md new file mode 100644 index 0000000..74a7b54 --- /dev/null +++ b/results/2026-10-12-rc4/RESULT.md @@ -0,0 +1,115 @@ +# 2026-10-12: faster single-stream decode, bit-exact with RC2 (engine sparkglm/atlas-20261012-rc4-layered) + +The image is `846ee9e1e501`. It runs engine `5c523903` on `sparkglm/atlas-20261012-rc4-layered`: the upstream candidate +`upstream/glm53-flash-20261012-rc4` at `ad047638` plus the unchanged SparkGLM-only layer. Its tree (`ae5e79f8`) is +identical to `exp/prose-rc4` at `c6089633`, the build measured below. + +## RC4.1 hotfix: long GLM tool calls + +The pin is now engine `301479a2` on `sparkglm/atlas-20261012-rc4.1-layered` (tree `ac5a080b`). It is RC4 plus one fix, +re-layered on `upstream/glm53-flash`. + +- **The bug.** A guard ends a tool call that stays in its envelope for more than 1,024 tokens (`MAX_TOOL_BODY_TOKENS`). + Parameter values are exempt, but the guard recognized them only by Qwen's `` token ids. On GLM every + value token counted as envelope, so any tool call longer than about 1,024 tokens (a large file write, say) ended + with `finish_reason` "length" and no tool call. The bug predates RC2. +- **The fix** (`ade7b012`): the engine resolves GLM's single-token `` and `` at startup and + tracks value bodies by them. Sampling is unchanged; the markers only gate the guard. +- **Everything else is RC4.** Against RC4's tree the pin adds only this spark-server change and four CI-only upstream + commits (formatting, a doc link, the strix-hip selector copy, an unused variable), none of which touches the decode + path. The speed and exactness receipts below were measured on RC4. + +**RC4.1 checked live through LLooM** (image `cfb463a952b5`, production settings): +- A single `write_file` call of 3,516 tokens (a 612-line file) returns intact with `finish_reason` "tool_calls". RC4 + cut the same request at 1,026 tokens and returned no tool call. +- The prompt-logprob hash is `246cd1331b41`, unchanged. +- Prose and code decode, 12 prompts × 256 tokens: 45.56 / 64.53 tok/s (RC4: 45.57 / 64.91). +- Four concurrent ~204K-token sessions: 12/12 and 8/8. +- [RigMark Quality](https://github.com/alexellis/rigmark-quality) 0.2 (suite `74709c7c`, temperature 0, 16K output + cap, 28 turns), artefacts passed out of 13 tasks: + + | Reasoning effort | RC4 | RC4.1 | + |---|---:|---:| + | low | 10 | **11** | + | default | 9 | **11** | + + - On RC4, four task runs ended on the cut ("model exhausted output token cap"). On RC4.1 none do. + - go-transfer-ledger now passes at both settings, and go-worker-pool at default. + - The remaining failures are the model's own: + - **arkade-add:** no edit before the turn budget ran out in a 6,116-line Go file, at every effort tried; + - **statement-redact:** fails the hidden checks. + - These are task diagnostics, not a model score. + +## What changed since RC2 + +Ten engine changes, each behind its own switch, and one profile change, all on in the profiles. None of them changes a +target output bit. + +- **All-reduce stages on the SMs** (`ATLAS_RDMA_ONESHOT_SM_STAGE=1`): the one-shot pair all-reduce stages its data with + the SMs inside the PDL chain instead of a copy-engine hop. +- **MoE routing in two launches** (`ATLAS_GLM_MOE_ROUTE_FUSE=1`) and **the MLA layer front in two launches** + (`ATLAS_GLM_MLA_FUSE=1`), down from a chain of small kernels each. +- **Verify dense FFN split across the pair** (`ATLAS_GLM_DENSE_TP_SPLIT=1`). +- **KDA record commits over a fixed grid** (`ATLAS_GLM_KDA_COMMIT_CTAS=96`). +- **Chunk-tiled MXFP8 twins** for q_b, o and the split verify head (`ATLAS_GLM_MXFP8_TILED=1`): each warp's chunk is + one contiguous KiB. Same values into the same MMAs in the same order. +- **Drafter:** threshold top-k and parallel merges in the DFlash2 selector (`ATLAS_GLM_DRAFT_TP_TOPK=1`), uploads that + do not drain the stream (`ATLAS_DFLASH_ASYNC_UPLOADS=1`), the selector's transition term weighted 0.6 + (`ATLAS_DFLASH2_EDGE=0.6`), NVFP4 twins of the drafter's delta projections (`ATLAS_DFLASH_CONV_TWIN=nvfp4`), and the + confidence width's full-width probe every 256th step instead of every 64th (`ATLAS_DFLASH_CONF_PROBE=256`, a profile + change). These move drafts and acceptance only; the canonical verify keeps the target's output exact. + +## Measurements + +Two DGX Sparks, production settings: `4x1m`, 0.91, display carveout, KV shard, disk prefix cache 48 GB. Greedy, +256 or 384 forced tokens, thinking off. The 12-prompt RC2 baseline is the RC2 engine with `ATLAS_DFLASH_CONF_PROBE=256`, the one +switch it already had. + +| C1 decode | This release | RC2 | +|---|---:|---:| +| Prose, 12 prompts × 256 tokens, tok/s (`raw/multi-*.json`) | **45.57** | 42.82 | +| Code, 12 prompts × 256 tokens, tok/s | **64.91** | 61.38 | +| Strict prose bursts, 8 prompts × 384 tokens, tok/s (`raw/bursts-*.json`) | **47.9** | 44.8 | + +Tokens per verify step are within 1% of RC2 (prose 2.50 vs 2.51, code 4.88 vs 4.85, bursts 2.51 vs 2.49): the gain is +step time, 52.6 ms against 55.9 ms per prose step in the bursts run. The bursts file has every token's arrival time. + +**Bit-exact with RC2.** +- The prompt-logprob hash of a fixed 48,875-token text is `246cd1331b41`, unchanged (`raw/lp-rc4-on.json`). +- The 500 arithmetic problems and 24 two-hop 24K needles: 524 of 524 outputs are byte-identical to RC2's + (`raw/qbig-rc4-on.jsonl` against `../2026-10-09-rc2/raw/qbig-rc2.jsonl`), so the scores are RC2's: 491/500 and 24/24. +- Determinism: four greedy prompts at C1, then all four concurrently, twice. Both C4 runs equal C1 on 4 of 4 + (`raw/rc4_on.json`). + +**Deployed through LLooM** (production settings above): +- Four concurrent ~204K-token sessions, three turns each: 12/12 exact answers, turns 2+ served from the prefix cache + (`raw/fill-prod.json`). The lowest MemAvailable on rank 0 during the fill was 4.4 GiB (`raw/prod-mem-rank0.log`). +- 45K-token multi-turn and a 35K-token replay: all correct, cached turns start in 0.7–0.9 s (`raw/pc-prod.json`). + +**Release suite through LLooM** (the same runs as RC2's receipts, same settings; RC2's numbers from +`../2026-10-09-rc2/`): + +| | This release | RC2 | +|---|---:|---:| +| RigMark decode, code / prose / structured, tok/s (median of 3, `raw/rig-decode.json`) | **72.3 / 38.3 / 91.7** | 65.5 / 36.5 / 88.8 | +| RigMark cold prefill 8K / 32K / 64K, TTFT (s) | 3.29 / 12.47 / 23.91 | 3.29 / 12.47 / 23.72 | +| RigMark staggered arrivals, prefill first: newcomer TTFT at 2 / 4 (s) (`raw/rig-conc.json`) | 2.80 / 3.54 | 2.80 / 3.54 | +| RigMark short code, 1 / 2 / 3 / 4 streams aggregate, tok/s (mean of 2 rounds) | 47.0 / **60.5** / 66.8 / **70.9** | 47.7 / 55.2 / 65.4 / 69.5 | +| Staggered C4 field guide, ~16K input (median of 5), s (`raw/field-16k/`) | **49.5** | 49.9 | +| Staggered C4 field guide, ~32K input (median of 3), s (`raw/field-32k/`) | **76.3** | 77.3 | +| Matrix (C1/C2 16K and 32K, C4 16K; sum of walls), s (`raw/matrix/`) | **132.1** | 136.3 | +| Quality probe: arithmetic / two-hop needle | 39/40 / 12/12 | 39/40 / 12/12 | + +- RigMark samples, so its output text and lengths differ between runs and engines; its decode rows are not an + equal-text comparison. Two structured runs came out compact (441 tokens: 91.3, 91.7 tok/s) and one pretty-printed + (694 tokens: 95.5). +- Prefill does not change: the new kernels run in decode and verify. The 64K row is within run-to-run spread. +- The matrix's C4 16K case is 41.1 s against 40.5 s; the C1 and C2 cases are faster. + +## Verify the receipts + +The JSON files are the probes' own output, unedited. + +```sh +cd results/2026-10-12-rc4 && sha256sum -c SHA256SUMS +``` diff --git a/results/2026-10-12-rc4/SHA256SUMS b/results/2026-10-12-rc4/SHA256SUMS new file mode 100644 index 0000000..32e1419 --- /dev/null +++ b/results/2026-10-12-rc4/SHA256SUMS @@ -0,0 +1,28 @@ +9d12fef8cc08d84c2e80aa9352e867aa6b6afa81921e81b8f5d9b6bc456822f5 raw/bursts-rc2-prose.json +9f97c0a4fbee45ea3e3ab1b76e20a0e257a3c2a8eda4f12e4e65e6d07047ae90 raw/bursts-rc4_on.json +1bc64b8dcba8cc26eee297fd7deed3254a293858dfd868c0196f8543bc28dac0 raw/field-16k/discarded-warmup.json +6c366fdc11c43e61123fed96bb1d1a734765170d005f49460ebd74336c0a838e raw/field-16k/field-guide-r1.json +45d1ba81a7445870b4f67e0a7dbfdb23478f8a239a591aac6eb64f56645ac460 raw/field-16k/field-guide-r2.json +48dae25a24e20b40f4e1231f542445bef41130ffd6923665d1798ee93fa05668 raw/field-16k/field-guide-r3.json +64c2f491c0a79e6cd500489605acc49ff9fa65fc1b7f7cac0460013f699076d8 raw/field-16k/field-guide-r4.json +db3e75ceebb0e6a0f89f1322c44b2123f1df6a21038d03666b5037e16a23f248 raw/field-16k/field-guide-r5.json +145a7e636b777d0128e59568e8544e6794305e383083a8f944274515d5f64440 raw/field-32k/discarded-warmup.json +13e73566a700f4a27ab4bb8879114f5fe8f58f14f869f14fba36e2c7805aa61e raw/field-32k/field-guide-r1.json +524dda5f790e85ad111b50dfcca315e850796abdeefb356ac27d252bebe4f876 raw/field-32k/field-guide-r2.json +2e9a96f8564d7589a95fc3bf8f59c284e606509e1e0cb3e97db3ac2899bad5e7 raw/field-32k/field-guide-r3.json +29c5749eba9e2f4ebcacf29605c6be26de43f8a6398ee6b642644711a986512d raw/fill-prod.json +11c8b9dca0e107ca5fa0d3bc902da1df9dbd9e2e0cee0317f10fd0898020f86a raw/lp-rc4-on.json +b78cfa5f60790b56306fee1aca251c2d5c0c4ad5e710965339090962ce626071 raw/matrix/c1-16k.json +83fab893bb7104ccfb0c7840e5b3b62b4be7a727bd06725d83496b76acd40284 raw/matrix/c1-32k.json +fdb4761dd2967ea34adbddc8cc066603c7e9fec4e64359c071f6affaae893224 raw/matrix/c2-16k.json +c9ea904ed30e07ef04ac348ccfbbac6f1fc791c1d50617629349dc78756edd10 raw/matrix/c2-32k.json +d0d1ffba811592f0dce1ad937c243d456b908c163884c8fbce4e2a8e9d21b24a raw/matrix/c4-16k.json +d96cff0a096ae80d5af0f91873a3339fa55a5789ff4b103ff617f95ba4315c65 raw/multi-rc3-base.json +b53f9bd1cef126d7599bf8b93ad8240e543a817e17265eef45c41dae1ec4951c raw/multi-rc4_on.json +808e9932a573133d58ac4c4cf1abbad933e14b013609f8a92a137502da327ec8 raw/pc-prod.json +cd3e426fb418a50133341e8cc26ea6dfdec12696f6f183bef513ed3eef63f3d3 raw/prod-mem-rank0.log +e9774ded3ced6495e896c9c8530f9399be3950bee49196c36bf62b37d56f15ef raw/qbig-rc4-on.jsonl +e7b3167e778d7c8d2ce99534f7c11b9e22059bfe65dad77d8363023da1b25764 raw/rc4_on.json +cda5dc6946b44288602c995f31ddc041ce317143e3fb1a24eecb0601070c5779 raw/rig-conc.card.txt 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true +} \ No newline at end of file diff --git a/results/2026-10-12-rc4/raw/matrix/c1-16k.json b/results/2026-10-12-rc4/raw/matrix/c1-16k.json new file mode 100644 index 0000000..58e0955 --- /dev/null +++ b/results/2026-10-12-rc4/raw/matrix/c1-16k.json @@ -0,0 +1 @@ +{"config": {"concurrency": 1, "disable_loop_watchdog": false, "enable_thinking": false, "exact_prompt_tokens": true, "max_output_tokens": 400, "max_output_tokens_by_request": [400], "min_output_tokens": 400, "min_output_tokens_by_request": [400], "prompt_salt": "mb-rc4-846ee-1791805691-c1-16k", "prompt_style": "isolation", "prompt_styles_by_request": ["isolation"], "requested_prompt_tokens": 16384, "requested_prompt_tokens_by_request": [16384], "stagger_ms": 1000}, "requests": [{"accepted_prediction_tokens": 310, "completion_tokens": 400, "decode_tok_s": 67.0912141416021, "effective_prefill_tok_s": 2565.9932637594875, "elapsed_s": 12.332179267017636, "error": null, "finish_reason": "length", "foreign_request_markers": [], "inter_event_gap_p95_s": 0.08022768999217078, "max_inter_event_gap_s": 0.09948714496567845, "output_chars": 1027, "output_preview": "Summarize: payload is \"alpha\" repeated many times. Include GLMISO-9891E84D8A3A0A849FC0 once and mention FINAL-FACT.**Summary:** The payload consists of the word", "output_sha256": "a4143fff21927d53207fd309cf21dae41121fccc2172b7f052dce1e3da48486e", "own_request_marker": true, "prompt_sha256": "df17013ef474b22386adb7c0120b8564c7b580a3a3da96ea03562b3f802e7686", "prompt_tokens": 16384, "reached_requested_completion_tokens": true, "rejected_prediction_tokens": 0, "request_id": 0, "requested_completion_tokens": 400, "scheduled_s": 0.0, "started_s": 0.0004627119633369148, "ttft_s": 6.3850518360268325, "visible_events": 399}], "summary": {"aggregate_decode_tok_s": 67.0912141416021, "aggregate_effective_prefill_tok_s": 2565.9932637594875, "decode_tok_s_p50": 67.0912141416021, "decode_tok_s_sum": 67.0912141416021, "effective_prefill_tok_s_p50": 2565.9932637594875, "successful": 1, "ttft_p50_s": 6.3850518360268325, "ttft_p95_s": 6.3850518360268325, "wall_s": 12.33314199798042}} diff --git a/results/2026-10-12-rc4/raw/matrix/c1-32k.json b/results/2026-10-12-rc4/raw/matrix/c1-32k.json new file mode 100644 index 0000000..11330b9 --- /dev/null +++ b/results/2026-10-12-rc4/raw/matrix/c1-32k.json @@ -0,0 +1 @@ +{"config": {"concurrency": 1, "disable_loop_watchdog": false, "enable_thinking": false, "exact_prompt_tokens": true, "max_output_tokens": 400, "max_output_tokens_by_request": [400], "min_output_tokens": 400, "min_output_tokens_by_request": [400], "prompt_salt": "mb-rc4-846ee-1791805691-c1-32k", "prompt_style": "isolation", "prompt_styles_by_request": ["isolation"], "requested_prompt_tokens": 32768, "requested_prompt_tokens_by_request": [32768], "stagger_ms": 1000}, "requests": [{"accepted_prediction_tokens": 310, "completion_tokens": 400, "decode_tok_s": 67.23716174986012, "effective_prefill_tok_s": 2621.616237006147, "elapsed_s": 18.433378056972288, "error": null, "finish_reason": "length", "foreign_request_markers": [], "inter_event_gap_p95_s": 0.07524748297873884, "max_inter_event_gap_s": 0.09820211702026427, "output_chars": 1325, "output_preview": "The payload is just \"alpha\" repeated many times, ending with FINAL-FACT. Summarize, repeat the token once, mention FINAL-FACT.**Summary:** The payload consists ", "output_sha256": "044233b32ffa30860dc1d6b1a2314debef71b2a4c14f4a2becc2d6bfc7bb9096", "own_request_marker": true, "prompt_sha256": "50a9e41c5ed905019216844a7bc259064cb3c940ef7ef3f4a76e92df45cf3823", "prompt_tokens": 32768, "reached_requested_completion_tokens": true, "rejected_prediction_tokens": 0, "request_id": 0, "requested_completion_tokens": 400, "scheduled_s": 0.0, "started_s": 0.000123982026707381, "ttft_s": 12.499159692961257, "visible_events": 399}], "summary": {"aggregate_decode_tok_s": 67.23716174986012, "aggregate_effective_prefill_tok_s": 2621.616237006147, "decode_tok_s_p50": 67.23716174986012, "decode_tok_s_sum": 67.23716174986012, "effective_prefill_tok_s_p50": 2621.616237006147, "successful": 1, "ttft_p50_s": 12.499159692961257, "ttft_p95_s": 12.499159692961257, "wall_s": 18.43419579201145}} diff --git a/results/2026-10-12-rc4/raw/matrix/c2-16k.json b/results/2026-10-12-rc4/raw/matrix/c2-16k.json new file mode 100644 index 0000000..42ff2ab --- /dev/null +++ b/results/2026-10-12-rc4/raw/matrix/c2-16k.json @@ -0,0 +1 @@ +{"config": {"concurrency": 2, "disable_loop_watchdog": false, "enable_thinking": false, "exact_prompt_tokens": true, "max_output_tokens": 400, "max_output_tokens_by_request": [400, 400], "min_output_tokens": 400, "min_output_tokens_by_request": [400, 400], "prompt_salt": "mb-rc4-846ee-1791805691-c2-16k", "prompt_style": "isolation", "prompt_styles_by_request": ["isolation", "isolation"], "requested_prompt_tokens": 16384, "requested_prompt_tokens_by_request": [16384, 16384], "stagger_ms": 1000}, "requests": [{"accepted_prediction_tokens": 273, "completion_tokens": 400, "decode_tok_s": 27.46391802327474, "effective_prefill_tok_s": 1734.0957355373387, "elapsed_s": 23.97630483697867, "error": null, "finish_reason": "length", "foreign_request_markers": [], "inter_event_gap_p95_s": 0.11798123602056876, "max_inter_event_gap_s": 3.3890322750085033, "output_chars": 1316, "output_preview": "The payload is just \"alpha\" repeated many times, ending with FINAL-FACT. Summarize, repeat the token once, mention FINAL-FACT.**Summary:** The payload consists ", "output_sha256": "3280467b8fbc5bbfda11a27c135664c82c7b16fdf2a0ac4bf315736a42c0447e", "own_request_marker": true, "prompt_sha256": "4783602e5df6705592259eb1ef848b6c871893bb863b84241d09c4e63906064a", "prompt_tokens": 16384, "reached_requested_completion_tokens": true, "rejected_prediction_tokens": 0, "request_id": 0, "requested_completion_tokens": 400, "scheduled_s": 0.0, "started_s": 0.0004817630397155881, "ttft_s": 9.448151946999133, "visible_events": 399}, {"accepted_prediction_tokens": 265, "completion_tokens": 400, "decode_tok_s": 34.16042168761348, "effective_prefill_tok_s": 1381.9002061794802, "elapsed_s": 23.53632201103028, "error": null, "finish_reason": "length", "foreign_request_markers": [], "inter_event_gap_p95_s": 0.11631068703718483, "max_inter_event_gap_s": 0.13148844498209655, "output_chars": 1359, "output_preview": "Summarize and comply.**Summary:** The payload consists of the word \"alpha\" repeated many hundreds of times, followed by the marker \"FINAL-FACT\". There is no oth", "output_sha256": "592a1a0af75253e55b3d7cabe59d488c55608f7cc9f49afcd7b25da3e180cee4", "own_request_marker": true, "prompt_sha256": "f7760eeb96b83ca51e28a14c7454385433787a0c5077e3be57f8308e8d8cd369", "prompt_tokens": 16384, "reached_requested_completion_tokens": true, "rejected_prediction_tokens": 0, "request_id": 1, "requested_completion_tokens": 400, "scheduled_s": 1.0, "started_s": 1.000722435011994, "ttft_s": 11.856138328032102, "visible_events": 399}], "summary": {"aggregate_decode_tok_s": 52.88827391846121, "aggregate_effective_prefill_tok_s": 2548.773647695734, "decode_tok_s_p50": 30.81216985544411, "decode_tok_s_sum": 61.62433971088822, "effective_prefill_tok_s_p50": 1557.9979708584094, "successful": 2, "ttft_p50_s": 10.652145137515618, "ttft_p95_s": 11.856138328032102, "wall_s": 24.537646099051926}} diff --git a/results/2026-10-12-rc4/raw/matrix/c2-32k.json b/results/2026-10-12-rc4/raw/matrix/c2-32k.json new file mode 100644 index 0000000..fdc196b --- /dev/null +++ b/results/2026-10-12-rc4/raw/matrix/c2-32k.json @@ -0,0 +1 @@ +{"config": {"concurrency": 2, "disable_loop_watchdog": false, "enable_thinking": false, "exact_prompt_tokens": true, "max_output_tokens": 400, "max_output_tokens_by_request": [400, 400], "min_output_tokens": 400, "min_output_tokens_by_request": [400, 400], "prompt_salt": "mb-rc4-846ee-1791805691-c2-32k", "prompt_style": "isolation", "prompt_styles_by_request": ["isolation", "isolation"], "requested_prompt_tokens": 32768, "requested_prompt_tokens_by_request": [32768, 32768], "stagger_ms": 1000}, "requests": [{"accepted_prediction_tokens": 283, "completion_tokens": 400, "decode_tok_s": 19.756945458862095, "effective_prefill_tok_s": 2108.5293418862334, "elapsed_s": 35.73611913603963, "error": null, "finish_reason": "length", "foreign_request_markers": [], "inter_event_gap_p95_s": 0.1236276010167785, "max_inter_event_gap_s": 3.428634786978364, "output_chars": 1518, "output_preview": "The payload is just \"alpha\" repeated many times, ending with FINAL-FACT. Summarize, repeat the token once, mention FINAL-FACT.**Summary:** The payload consists ", "output_sha256": "01a9573fa82964c5b47197f0403633b76e93d52dde390b03fa6cdbe3eb719119", "own_request_marker": true, "prompt_sha256": "0d75584a762cd392990bbc5b963457bc2c3397da54246e6f200ee717625ee70c", "prompt_tokens": 32768, "reached_requested_completion_tokens": true, "rejected_prediction_tokens": 0, "request_id": 0, "requested_completion_tokens": 400, "scheduled_s": 0.0, "started_s": 0.000522441987413913, "ttft_s": 15.540689593006391, "visible_events": 397}, {"accepted_prediction_tokens": 306, "completion_tokens": 400, "decode_tok_s": 43.06187765442146, "effective_prefill_tok_s": 1355.5607393394212, "elapsed_s": 33.43875854596263, "error": null, "finish_reason": "length", "foreign_request_markers": [], "inter_event_gap_p95_s": 0.12198742595501244, "max_inter_event_gap_s": 0.12939390202518553, "output_chars": 1326, "output_preview": "The payload is just \"alpha\" repeated many times, plus FINAL-FACT. Summarize, repeat the token once, mention FINAL-FACT.**Summary:** The payload consists of the ", "output_sha256": "fb88b93641f22a9bbcf723873905e61b2f69c440f2deda3fb1168071d40ab999", "own_request_marker": true, "prompt_sha256": "34d0262bbda91d1fb1f69260d55df82b736503c1284edd68f13ac3aaf6c9f673", "prompt_tokens": 32768, "reached_requested_completion_tokens": true, "rejected_prediction_tokens": 0, "request_id": 1, "requested_completion_tokens": 400, "scheduled_s": 1.0, "started_s": 1.000173258013092, "ttft_s": 24.173022313974798, "visible_events": 399}], "summary": {"aggregate_decode_tok_s": 39.51389091772419, "aggregate_effective_prefill_tok_s": 2603.458109575777, "decode_tok_s_p50": 31.40941155664178, "decode_tok_s_sum": 62.81882311328356, "effective_prefill_tok_s_p50": 1732.0450406128273, "successful": 2, "ttft_p50_s": 19.856855953490594, "ttft_p95_s": 24.173022313974798, "wall_s": 35.73745770001551}} diff --git a/results/2026-10-12-rc4/raw/matrix/c4-16k.json b/results/2026-10-12-rc4/raw/matrix/c4-16k.json new file mode 100644 index 0000000..31459bd --- /dev/null +++ b/results/2026-10-12-rc4/raw/matrix/c4-16k.json @@ -0,0 +1 @@ +{"config": {"concurrency": 4, "disable_loop_watchdog": false, "enable_thinking": false, "exact_prompt_tokens": true, "max_output_tokens": 400, "max_output_tokens_by_request": [400, 400, 400, 400], "min_output_tokens": 400, "min_output_tokens_by_request": [400, 400, 400, 400], "prompt_salt": "mb-rc4-846ee-1791805691-c4-16k", "prompt_style": "isolation", "prompt_styles_by_request": ["isolation", "isolation", "isolation", "isolation"], "requested_prompt_tokens": 16384, "requested_prompt_tokens_by_request": [16384, 16384, 16384, 16384], "stagger_ms": 1000}, "requests": [{"accepted_prediction_tokens": 304, "completion_tokens": 400, "decode_tok_s": 15.6637945166899, "effective_prefill_tok_s": 1050.0932407125526, "elapsed_s": 41.07517901196843, "error": null, "finish_reason": "length", "foreign_request_markers": [], "inter_event_gap_p95_s": 0.1868545999750495, "max_inter_event_gap_s": 3.46061531099258, "output_chars": 1162, "output_preview": "The payload is just \"alpha\" repeated many times, ending with FINAL-FACT. The user asks to repeat \"GLMISO-9891E84D8A3A0A849FC0\" exactly once \u2014 that string doesn'", "output_sha256": "d9a7c622a37d312135bf47a1dd5a2859bfd2f4d6cb0a9c123a0b50efa4de2d94", "own_request_marker": true, "prompt_sha256": "a3cfca105328a0750bf0175fd31fe67eb6db00926ca972271a68773fb9294da4", "prompt_tokens": 16384, "reached_requested_completion_tokens": true, "rejected_prediction_tokens": 0, "request_id": 0, "requested_completion_tokens": 400, "scheduled_s": 0.0, "started_s": 0.00017602602019906044, "ttft_s": 15.602424017968588, "visible_events": 399}, {"accepted_prediction_tokens": 303, "completion_tokens": 400, "decode_tok_s": 18.298454140306653, "effective_prefill_tok_s": 905.0658646871774, "elapsed_s": 39.90767056099139, "error": null, "finish_reason": "length", "foreign_request_markers": [], "inter_event_gap_p95_s": 0.1866570040001534, "max_inter_event_gap_s": 3.4604017040110193, "output_chars": 1380, "output_preview": "# Summary\n\n**Payload:** The payload consists of the word \"alpha\" repeated many times (approximately 1,600 occurrences), with no other meaningful content.\n\n**Req", "output_sha256": "320adf099a7a78e2554e48a5888f8319c4dce90eb5acd69e355f561e96b7b914", "own_request_marker": true, "prompt_sha256": "ffbd4f9b9cfe23c5656a179bb428c50e888087e9e94a7f250cd934a58a3d533a", "prompt_tokens": 16384, "reached_requested_completion_tokens": true, "rejected_prediction_tokens": 0, "request_id": 1, "requested_completion_tokens": 400, "scheduled_s": 1.0, "started_s": 1.0005854819901288, "ttft_s": 18.10254992399132, "visible_events": 399}, {"accepted_prediction_tokens": 315, "completion_tokens": 400, "decode_tok_s": 23.040181803791256, "effective_prefill_tok_s": 803.0777549108788, "elapsed_s": 37.719083119009156, "error": null, "finish_reason": "length", "foreign_request_markers": [], "inter_event_gap_p95_s": 0.18613045394886285, "max_inter_event_gap_s": 3.4609143920242786, "output_chars": 1103, "output_preview": "The payload is just \"alpha\" repeated many times, ending with FINAL-FACT. Summarize, repeat the string once.**Summary:** The payload consists of the word \"alpha\"", "output_sha256": "860051a733a7f10874d90b5a585199feaea5c2fbde3a633894038dc587251c18", "own_request_marker": true, "prompt_sha256": "eb598442e2efc4ac82ba7e6e88320e8b53ebdc74a692093e1cb644ec3431fa02", "prompt_tokens": 16384, "reached_requested_completion_tokens": true, "rejected_prediction_tokens": 0, "request_id": 2, "requested_completion_tokens": 400, "scheduled_s": 2.0, "started_s": 2.0002370000001974, "ttft_s": 20.401511434989516, "visible_events": 399}, {"accepted_prediction_tokens": 323, "completion_tokens": 400, "decode_tok_s": 29.329197611882858, "effective_prefill_tok_s": 717.1691661803955, "elapsed_s": 36.44956782500958, "error": null, "finish_reason": "length", "foreign_request_markers": [], "inter_event_gap_p95_s": 0.18597811897052452, "max_inter_event_gap_s": 0.19412927696248516, "output_chars": 1015, "output_preview": "The payload is just \"alpha\" repeated many times, ending with FINAL-FACT. Summarize, repeat the code once, mention FINAL-FACT.**Summary:** The payload consists o", "output_sha256": "09408440485b639fddde6506554e41d7344334dc8eb21b6237296ae1b5f6354a", "own_request_marker": true, "prompt_sha256": "91256d44235056a3b5f2ba1d1fc6bc7c95c089dcf5a36d4f453d5dca96960efd", "prompt_tokens": 16384, "reached_requested_completion_tokens": true, "rejected_prediction_tokens": 0, "request_id": 3, "requested_completion_tokens": 400, "scheduled_s": 3.0, "started_s": 3.0001651589991525, "ttft_s": 22.84537703602109, "visible_events": 399}], "summary": {"aggregate_decode_tok_s": 62.6551780667596, "aggregate_effective_prefill_tok_s": 2535.6963244965154, "decode_tok_s_p50": 20.669317972048955, "decode_tok_s_sum": 86.33162807267067, "effective_prefill_tok_s_p50": 854.0718097990281, "successful": 4, "ttft_p50_s": 19.252030679490417, "ttft_p95_s": 22.84537703602109, "wall_s": 41.076361485000234}} diff --git a/results/2026-10-12-rc4/raw/multi-rc3-base.json b/results/2026-10-12-rc4/raw/multi-rc3-base.json new file mode 100644 index 0000000..7246e0b --- /dev/null +++ b/results/2026-10-12-rc4/raw/multi-rc3-base.json @@ -0,0 +1 @@ +{"prose": {"tok_s": 42.82, "tok_step_mean": 2.512, "n": 12}, "code": {"tok_s": 61.38, "tok_step_mean": 4.851, "n": 12}} \ No newline at end of file diff --git a/results/2026-10-12-rc4/raw/multi-rc4_on.json b/results/2026-10-12-rc4/raw/multi-rc4_on.json new file mode 100644 index 0000000..2b345fd --- /dev/null +++ b/results/2026-10-12-rc4/raw/multi-rc4_on.json @@ -0,0 +1 @@ +{"prose": {"tok_s": 45.57, "tok_step_mean": 2.501, "n": 12}, "code": {"tok_s": 64.91, "tok_step_mean": 4.881, "n": 12}} \ No newline at end of file diff --git a/results/2026-10-12-rc4/raw/pc-prod.json b/results/2026-10-12-rc4/raw/pc-prod.json new file mode 100644 index 0000000..52e444d --- /dev/null +++ b/results/2026-10-12-rc4/raw/pc-prod.json @@ -0,0 +1,77 @@ +{ + "tag": "prod", + "replay": [ + { + "ttft": 13.87, + "wall": 13.99, + "text": "407943", + "prompt_tokens": 35111, + "cached": 0, + "correct": true + }, + { + "ttft": 0.39, + "wall": 0.51, + "text": "407943", + "prompt_tokens": 35111, + "cached": 35104, + "correct": true + } + ], + "turns": [ + { + "ttft": 17.26, + "wall": 17.37, + "text": "627379", + "prompt_tokens": 45419, + "cached": 0, + "correct": true, + "turn": 1 + }, + { + "ttft": 0.9, + "wall": 0.96, + "text": "473778", + "prompt_tokens": 45447, + "cached": 45408, + "correct": true, + "turn": 2 + }, + { + "ttft": 0.88, + "wall": 0.93, + "text": "365683", + "prompt_tokens": 45475, + "cached": 45440, + "correct": true, + "turn": 3 + }, + { + "ttft": 0.71, + "wall": 0.83, + "text": "822535", + "prompt_tokens": 45501, + "cached": 45472, + "correct": true, + "turn": 4 + }, + { + "ttft": 0.92, + "wall": 0.99, + "text": "266033", + "prompt_tokens": 45528, + "cached": 45488, + "correct": true, + "turn": 5 + }, + { + "ttft": 0.68, + "wall": 0.81, + "text": "696467", + "prompt_tokens": 45556, + "cached": 45520, + "correct": true, + "turn": 6 + } + ] +} \ No newline at end of file diff --git a/results/2026-10-12-rc4/raw/prod-mem-rank0.log b/results/2026-10-12-rc4/raw/prod-mem-rank0.log new file mode 100644 index 0000000..b93c6d4 --- /dev/null +++ b/results/2026-10-12-rc4/raw/prod-mem-rank0.log @@ -0,0 +1,71 @@ +00:42:51 5061756 +00:42:56 4601400 +00:43:01 4643764 +00:43:06 4671656 +00:43:11 4647036 +00:43:16 4657612 +00:43:21 4642928 +00:43:26 4635540 +00:43:31 4643524 +00:43:36 4649440 +00:43:41 4653148 +00:43:46 4648676 +00:43:51 4657660 +00:43:56 4637796 +00:44:01 4644152 +00:44:06 4650368 +00:44:11 4644588 +00:44:16 4631736 +00:44:21 4621248 +00:44:26 4685876 +00:44:31 4688908 +00:44:36 4673088 +00:44:41 4669848 +00:44:46 4656556 +00:44:51 4645732 +00:44:56 4641300 +00:45:01 4645108 +00:45:06 4641824 +00:45:11 4639660 +00:45:16 4642896 +00:45:21 4631280 +00:45:26 4630476 +00:45:31 4628376 +00:45:36 4627240 +00:45:41 4755728 +00:45:46 4670748 +00:45:51 4680328 +00:45:56 4665684 +00:46:01 4677812 +00:46:06 4684136 +00:46:11 4686608 +00:46:16 4686168 +00:46:21 4683420 +00:46:26 4676760 +00:46:31 4675308 +00:46:36 4661328 +00:46:41 4660804 +00:46:46 4668100 +00:46:51 4667040 +00:46:56 4741632 +00:47:01 4671296 +00:47:06 4675140 +00:47:11 4678176 +00:47:16 4667764 +00:47:21 4667800 +00:47:26 4666304 +00:47:31 4671208 +00:47:36 4678236 +00:47:41 4669196 +00:47:46 4662272 +00:47:51 4658524 +00:47:56 4641808 +00:48:01 4654968 +00:48:06 4710876 +00:48:11 4837500 +00:48:16 4833328 +00:48:21 4845860 +00:48:26 4854260 +00:48:31 4851236 +00:48:36 4862164 +00:48:41 4864888 diff --git a/results/2026-10-12-rc4/raw/qbig-rc4-on.jsonl b/results/2026-10-12-rc4/raw/qbig-rc4-on.jsonl new file mode 100644 index 0000000..0d5b394 --- /dev/null +++ b/results/2026-10-12-rc4/raw/qbig-rc4-on.jsonl @@ -0,0 +1,524 @@ +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "127479", "out": "4272 \u00d7 72 = 307,584\n3278 \u00d7 55 = 180,290\n307,584 \u2212 180,290 = 127,294\n127,294 + 185 = 127,479\n\nAnswer: 127479"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-304216", "out": "4157 \u00d7 96 = 399,072\n9781 \u00d7 72 = 704,232\n399,072 \u2212 704,232 = \u2212305,160\n\u2212305,160 + 944 = \u2212304,216\n\nAnswer: -304216"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-273978", "out": "4784 \u00d7 25 = 119,600\n8564 \u00d7 46 = 393,944\n119,600 \u2212 393,944 = \u2212274,344\n\u2212274,344 + 366 = \u2212273,978\n\nAnswer: -273978"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-270986", "out": "6599 \u00d7 51 = 336,549\n9807 \u00d7 62 = 608,034\n336,549 \u2212 608,034 = \u2212271,485\n\u2212271,485 + 499 = \u2212270,986\n\nAnswer: -270986"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "707562", "out": "8743 \u00d7 95 = 830,585\n1545 \u00d7 80 = 123,600\n830,585 \u2212 123,600 = 706,985\n706,985 + 577 = 707,562\n\nAnswer: 707562"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-280189", "out": "7620 \u00d7 38 = 289,560\n8383 \u00d7 68 = 570,044\n289,560 \u2212 570,044 + 295 = \u2212280,189\n\nAnswer: -280189"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-98357", "out": "9614 \u00d7 47 = 451,858\n6557 \u00d7 84 = 550,788\n451,858 \u2212 550,788 = \u221298,930\n\u221298,930 + 573 = \u221298,357\n\nAnswer: -98357"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "80466", "out": "6920 \u00d7 56 = 387,520\n4101 \u00d7 75 = 307,575\n387,520 \u2212 307,575 = 79,945\n79,945 + 521 = 80,466\n\nAnswer: 80466"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-623975", "out": "2497 \u00d7 40 = 99,880\n8836 \u00d7 82 = 706,880 + 17,672 = 724,552\n99,880 \u2212 724,552 + 697 = \u2212623,975\n\nAnswer: -623975"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-141392", "out": "6815 \u00d7 68 = 463,420\n9767 \u00d7 62 = 605,554\n463,420 \u2212 605,554 = \u2212142,134\n\u2212142,134 + 742 = \u2212141,392\n\nAnswer: -141392"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "92995", "out": "2536 \u00d7 66 = 167,376\n2996 \u00d7 25 = 74,900\n167,376 \u2212 74,900 = 92,476\n92,476 + 519 = 92,995\n\nAnswer: 92995"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-547968", "out": "1240 \u00d7 62 = 76,880\n8021 \u00d7 78 = 625,638\n76,880 \u2212 625,638 + 790 = \u2212547,968\n\nAnswer: -547968"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "235212", "out": "9224 \u00d7 38 = 350,512\n1484 \u00d7 78 = 115,752\n350,512 \u2212 115,752 = 234,760\n234,760 + 452 = 235,212\n\nAnswer: 235212"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "673667", "out": "9821 \u00d7 85 = 834,785\n7022 \u00d7 23 = 161,506\n834,785 \u2212 161,506 = 673,279\n673,279 + 388 = 673,667\n\nAnswer: 673667"} +{"tag": "rc4-on", "kind": "arith", "ok": false, "answer": "56886", "out": "3867 \u00d7 53 = 204,851\n9878 \u00d7 15 = 148,170\n204,851 \u2212 148,170 = 56,681\n56,681 + 105 = 56,786\n\nAnswer: 56786"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "143395", "out": "7180 \u00d7 55 = 394,900\n9685 \u00d7 26 = 251,810\n394,900 \u2212 251,810 = 143,090\n143,090 + 305 = 143,395\n\nAnswer: 143395"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "318725", "out": "5848 \u00d7 92 = 538,016\n9544 \u00d7 23 = 219,512\n538,016 \u2212 219,512 = 318,504\n318,504 + 221 = 318,725\n\nAnswer: 318725"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-39725", "out": "3296 \u00d7 86 = 283,456\n7527 \u00d7 43 = 323,661\n283,456 \u2212 323,661 + 480 = \u221239,725\n\nAnswer: -39725"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-334277", "out": "1631 \u00d7 22 = 35,882\n7266 \u00d7 51 = 370,566\n35,882 \u2212 370,566 + 407 = \u2212334,277\n\nAnswer: -334277"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "96722", "out": "4819 \u00d7 58 = 279,502\n4362 \u00d7 42 = 183,204\n279,502 \u2212 183,204 = 96,298\n96,298 + 424 = 96,722\n\nAnswer: 96722"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "246652", "out": "7373 \u00d7 81 = 597,213\n4681 \u00d7 75 = 351,075\n597,213 \u2212 351,075 = 246,138\n246,138 + 514 = 246,652\n\nAnswer: 246652"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-12168", "out": "3021 \u00d7 30 = 90,630\n1754 \u00d7 59 = 103,486\n90,630 \u2212 103,486 + 688 = \u221212,856 + 688 = \u221212,168\n\nAnswer: -12168"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-178131", "out": "8963 \u00d7 35 = 313,705\n6316 \u00d7 78 = 492,648\n313,705 \u2212 492,648 = \u2212178,943\n\u2212178,943 + 812 = \u2212178,131\n\nAnswer: -178131"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-141191", "out": "2009 \u00d7 32 = 64,288\n3495 \u00d7 59 = 206,205\n64,288 \u2212 206,205 + 726 = \u2212141,191\n\nAnswer: -141191"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "413187", "out": "8261 \u00d7 87 = 718,707\n8274 \u00d7 37 = 306,138\n718,707 \u2212 306,138 = 412,569\n412,569 + 618 = 413,187\n\nAnswer: 413187"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "174429", "out": "9618 \u00d7 46 = 442,428\n9575 \u00d7 28 = 268,100\n442,428 \u2212 268,100 = 174,328\n174,328 + 101 = 174,429\n\nAnswer: 174429"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "694177", "out": "9396 \u00d7 89 = 836,244\n2801 \u00d7 51 = 142,851\n836,244 \u2212 142,851 = 693,393\n693,393 + 784 = 694,177\n\nAnswer: 694177"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-404207", "out": "3082 \u00d7 43 = 132,526\n8808 \u00d7 61 = 537,288\n132,526 \u2212 537,288 = \u2212404,762\n\u2212404,762 + 555 = \u2212404,207\n\nAnswer: -404207"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-5781", "out": "8998 \u00d7 16 = 143,968\n7516 \u00d7 20 = 150,320\n143,968 \u2212 150,320 = \u22126,352\n\u22126,352 + 571 = \u22125,781\n\nAnswer: -5781"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "195071", "out": "8437 \u00d7 43 = 362,791\n9878 \u00d7 17 = 167,926\n362,791 \u2212 167,926 = 194,865\n194,865 + 206 = 195,071\n\nAnswer: 195071"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "498575", "out": "Step 1: 8841 \u00d7 74 = 654,234\nStep 2: 2270 \u00d7 69 = 156,630\nStep 3: 654,234 \u2212 156,630 = 497,604\nStep 4: 497,604 + 971 = 498,575\n\nAnswer: 498575"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-298374", "out": "5733 \u00d7 37 = 212,121\n6557 \u00d7 78 = 511,446\n212,121 \u2212 511,446 = \u2212299,325\n\u2212299,325 + 951 = \u2212298,374\n\nAnswer: -298374"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-285578", "out": "3759 \u00d7 30 = 112,770\n4748 \u00d7 84 = 379,840 + 18,992 = 398,832\n112,770 \u2212 398,832 + 484 = \u2212285,578\n\nAnswer: -285578"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "372227", "out": "9482 \u00d7 71 = 673,222\n5389 \u00d7 56 = 301,784\n673,222 \u2212 301,784 = 371,438\n371,438 + 789 = 372,227\n\nAnswer: 372227"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "294401", "out": "9313 \u00d7 35 = 325,955\n2445 \u00d7 13 = 31,785\n325,955 \u2212 31,785 = 294,170\n294,170 + 231 = 294,401\n\nAnswer: 294401"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "8632", "out": "9 = 7123 \u00d7 60 \u2212 7123 = 427380 \u2212 7123 = 420257\n6538 \u00d7 63 = 6538 \u00d7 60 + 6538 \u00d7 3 = 392280 + 19614 = 411894\n420257 \u2212 411894 + 269 = 8363 + 269 = 8632\n\nAnswer: 8632"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-175422", "out": "1542 \u00d7 70 = 107,940\n3341 \u00d7 85 = 283,985\n107,940 \u2212 283,985 = \u2212176,045\n\u2212176,045 + 623 = \u2212175,422\n\nAnswer: -175422"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "279720", "out": "7878 \u00d7 84 = 661,752\n7498 \u00d7 51 = 382,398\n661,752 \u2212 382,398 = 279,354\n279,354 + 366 = 279,720\n\nAnswer: 279720"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "9309", "out": "4696 \u00d7 16 = 75136\n5089 \u00d7 13 = 66157\n75136 \u2212 66157 = 8979\n8979 + 330 = 9309\n\nAnswer: 9309"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-667458", "out": "1092 \u00d7 23 = 25116\n8356 \u00d7 83 = 693548\n25116 \u2212 693548 + 974 = \u2212667458\n\nAnswer: -667458"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-18250", "out": "2381 \u00d7 44 = 104,764\n4573 \u00d7 27 = 123,471\n104,764 \u2212 123,471 = \u221218,707\n\u221218,707 + 457 = \u221218,250\n\nAnswer: -18250"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-255590", "out": "3537 \u00d7 48 = 169,776\n8526 \u00d7 50 = 426,300\n169,776 \u2212 426,300 = \u2212256,524\n\u2212256,524 + 934 = \u2212255,590\n\nAnswer: -255590"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "8901", "out": "7352 \u00d7 26 = 191,152\n6531 \u00d7 28 = 182,868\n191,152 \u2212 182,868 = 8,284\n8,284 + 617 = 8,901\n\nAnswer: 8901"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "207838", "out": "4139 \u00d7 82 = 339,398\n3384 \u00d7 39 = 131,976\n339,398 \u2212 131,976 = 207,422\n207,422 + 416 = 207,838\n\nAnswer: 207838"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "118031", "out": "7744 \u00d7 44 = 340,736\n3096 \u00d7 72 = 222,912\n340,736 \u2212 222,912 = 117,824\n117,824 + 207 = 118,031\n\nAnswer: 118031"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-206328", "out": "4509 \u00d7 42 = 189,378\n4456 \u00d7 89 = 396,584\n189,378 \u2212 396,584 = \u2212207,206\n\u2212207,206 + 878 = \u2212206,328\n\nAnswer: -206328"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "156178", "out": "2543 \u00d7 78 = 198,354\n1470 \u00d7 29 = 42,630\n198,354 \u2212 42,630 = 155,724\n155,724 + 454 = 156,178\n\nAnswer: 156178"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-444244", "out": "\u00d7 50 \u2212 5621 = 281050 \u2212 5621 = 275429\n8674 \u00d7 83 = 8674 \u00d7 80 + 8674 \u00d7 3 = 693920 + 26022 = 719942\n275429 \u2212 719942 + 269 = \u2212444513 + 269 = \u2212444244\n\nAnswer: -444244"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "578326", "out": "8557 \u00d7 91 = 778,687\n2683 \u00d7 75 = 201,225\n778,687 \u2212 201,225 = 577,462\n577,462 + 864 = 578,326\n\nAnswer: 578326"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-72649", "out": "1988 \u00d7 64 = 127232\n3516 \u00d7 57 = 200412\n127232 \u2212 200412 + 531 = \u221272649\n\nAnswer: -72649"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "82613", "out": "5956 \u00d7 37 = 220,372\n3074 \u00d7 45 = 138,330\n220,372 \u2212 138,330 = 82,042\n82,042 + 571 = 82,613\n\nAnswer: 82613"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-661426", "out": "1565 \u00d7 86 = 134,590\n8952 \u00d7 89 = 796,728\n134,590 \u2212 796,728 = \u2212662,138\n\u2212662,138 + 712 = \u2212661,426\n\nAnswer: -661426"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "353825", "out": "4485 \u00d7 92 = 412,620\n1167 \u00d7 51 = 59,517\n412,620 \u2212 59,517 = 353,103\n353,103 + 722 = 353,825\n\nAnswer: 353825"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-478664", "out": "1185 \u00d7 53 = 62,805\n9682 \u00d7 56 = 542,192\n62,805 \u2212 542,192 + 723 = \u2212478,664\n\nAnswer: -478664"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-369818", "out": "3056 \u00d7 18 = 55008\n5592 \u00d7 76 = 424992\n55008 \u2212 424992 + 166 = \u2212369818\n\nAnswer: -369818"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-196206", "out": "4991 \u00d7 64 = 319,424\n9379 \u00d7 55 = 515,845\n319,424 \u2212 515,845 + 215 = \u2212196,206\n\nAnswer: -196206"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "109349", "out": "6059 \u00d7 43 = 260,537\n9495 \u00d7 16 = 151,920\n260,537 \u2212 151,920 = 108,617\n108,617 + 732 = 109,349\n\nAnswer: 109349"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-508849", "out": "3531 \u00d7 29 = 102,399\n7030 \u00d7 87 = 611,610\n102,399 \u2212 611,610 + 362 = \u2212508,849\n\nAnswer: -508849"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-215793", "out": "8739 \u00d7 46 = 401,994\n6644 \u00d7 93 = 617,892\n401,994 \u2212 617,892 = \u2212215,898\n\u2212215,898 + 105 = \u2212215,793\n\nAnswer: -215793"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-149639", "out": "2739 \u00d7 81 = 221,859\n7448 \u00d7 50 = 372,400\n221,859 \u2212 372,400 + 902 = \u2212149,639\n\nAnswer: -149639"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-493509", "out": "1069 \u00d7 72 = 76,968\n9681 \u00d7 59 = 571,179\n76,968 \u2212 571,179 + 702 = \u2212493,509\n\nAnswer: -493509"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "247520", "out": "9836 \u00d7 41 = 403,276\n3329 \u00d7 47 = 156,463\n403,276 \u2212 156,463 = 246,813\n246,813 + 707 = 247,520\n\nAnswer: 247520"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-342401", "out": "8494 \u00d7 19 = 161,386\n8137 \u00d7 62 = 504,494\n161,386 \u2212 504,494 = \u2212343,108\n\u2212343,108 + 707 = \u2212342,401\n\nAnswer: -342401"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "140848", "out": "9208 \u00d7 64 = 589,312\n8011 \u00d7 56 = 448,616\n589,312 \u2212 448,616 = 140,696\n140,696 + 152 = 140,848\n\nAnswer: 140848"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-56583", "out": "9266 \u00d7 29 = 268,714\n8153 \u00d7 40 = 326,120\n268,714 \u2212 326,120 = \u221257,406\n\u221257,406 + 823 = \u221256,583\n\nAnswer: -56583"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "102922", "out": "4941 \u00d7 49 = 4941 \u00d7 50 \u2212 4941 = 247050 \u2212 4941 = 242109\n1700 \u00d7 82 = 139400\n242109 \u2212 139400 + 213 = 102709 + 213 = 102922\n\nAnswer: 102922"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "422652", "out": "6485 \u00d7 79 = 512,315\n6452 \u00d7 14 = 90,328\n512,315 \u2212 90,328 = 421,987\n421,987 + 665 = 422,652\n\nAnswer: 422652"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-841852", "out": "1084 \u00d7 19 = 20,596\n9590 \u00d7 90 = 863,100\n20,596 \u2212 863,100 + 652 = \u2212841,852\n\nAnswer: -841852"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-190731", "out": "2036 \u00d7 60 = 122,160\n6817 \u00d7 46 = 313,582\n122,160 \u2212 313,582 = \u2212191,422\n\u2212191,422 + 691 = \u2212190,731\n\nAnswer: -190731"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-442645", "out": "5649 \u00d7 20 = 112,980\n6543 \u00d7 85 = 556,155\n112,980 \u2212 556,155 + 530 = \u2212442,645\n\nAnswer: -442645"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-436967", "out": "2764 \u00d7 68 = 187,952\n9071 \u00d7 69 = 625,899\n187,952 \u2212 625,899 = \u2212437,947\n\u2212437,947 + 980 = \u2212436,967\n\nAnswer: -436967"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-9608", "out": "7977 \u00d7 22 = 175,494\n4641 \u00d7 40 = 185,640\n175,494 \u2212 185,640 = \u221210,146\n\u221210,146 + 538 = \u22129,608\n\nAnswer: -9608"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-276130", "out": "6549 \u00d7 19 = 124,431\n4502 \u00d7 89 = 400,678\n124,431 \u2212 400,678 + 117 = \u2212276,130\n\nAnswer: -276130"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-86435", "out": "2979 \u00d7 18 = 53,622\n3117 \u00d7 45 = 140,265\n53,622 \u2212 140,265 + 208 = \u221286,435\n\nAnswer: -86435"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-62783", "out": "7101 \u00d7 50 = 355,050\n4756 \u00d7 88 = 418,528\n355,050 \u2212 418,528 = \u221263,478\n\u221263,478 + 695 = \u221262,783\n\nAnswer: -62783"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-243871", "out": "8751 \u00d7 35 = 306,285\n7987 \u00d7 69 = 551,103\n306,285 \u2212 551,103 = \u2212244,818\n\u2212244,818 + 947 = \u2212243,871\n\nAnswer: -243871"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-3678", "out": "6262 \u00d7 83 = 519,746\n6802 \u00d7 77 = 523,754\n519,746 \u2212 523,754 = \u22124,008\n\u22124,008 + 330 = \u22123,678\n\nAnswer: -3678"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "70039", "out": "6729 \u00d7 26 = 174,954\n4232 \u00d7 25 = 105,800\n174,954 \u2212 105,800 = 69,154\n69,154 + 885 = 70,039\n\nAnswer: 70039"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "217969", "out": "7403 \u00d7 39 = 288,717\n1578 \u00d7 45 = 71,010\n288,717 \u2212 71,010 = 217,707\n217,707 + 262 = 217,969\n\nAnswer: 217969"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-535832", "out": "3101 \u00d7 21 = 65,121\n7078 \u00d7 85 = 601,630\n65,121 \u2212 601,630 + 677 = \u2212535,832\n\nAnswer: -535832"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-399527", "out": "2072 \u00d7 21 = 43,512\n5412 \u00d7 82 = 443,784\n43,512 \u2212 443,784 + 745 = \u2212399,527\n\nAnswer: -399527"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "699635", "out": "9260 \u00d7 81 = 750,060\n1746 \u00d7 29 = 50,634\n750,060 \u2212 50,634 = 699,426\n699,426 + 209 = 699,635\n\nAnswer: 699635"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-28431", "out": "7702 \u00d7 24 = 184,848\n3395 \u00d7 63 = 213,885\n184,848 \u2212 213,885 + 606 = \u221228,431\n\nAnswer: -28431"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-508214", "out": "3937 \u00d7 22 = 86,614\n6761 \u00d7 88 = 594,968\n86,614 \u2212 594,968 + 140 = \u2212508,214\n\nAnswer: -508214"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-34371", "out": "1796 \u00d7 59 = 105,964\n1579 \u00d7 89 = 140,531\n105,964 \u2212 140,531 + 196 = \u221234,371\n\nAnswer: -34371"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-436419", "out": "4693 \u00d7 28 = 131,404\n7016 \u00d7 81 = 568,296\n131,404 \u2212 568,296 + 473 = \u2212436,419\n\nAnswer: -436419"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-258818", "out": "7592 \u00d7 68 = 516,256\n8622 \u00d7 90 = 775,980\n516,256 \u2212 775,980 + 906 = \u2212258,818\n\nAnswer: -258818"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-162977", "out": "2721 \u00d7 52 = 141,492\n4236 \u00d7 72 = 304,992\n141,492 \u2212 304,992 = \u2212163,500\n\u2212163,500 + 523 = \u2212162,977\n\nAnswer: -162977"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-17547", "out": "3533 \u00d7 37 = 130,721\n6212 \u00d7 24 = 149,088\n130,721 \u2212 149,088 + 820 = \u221218,367 + 820 = \u221217,547\n\nAnswer: -17547"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "429633", "out": "7388 \u00d7 90 = 664,920\n3863 \u00d7 61 = 235,643\n664,920 \u2212 235,643 = 429,277\n429,277 + 356 = 429,633\n\nAnswer: 429633"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "230233", "out": "5797 \u00d7 52 = 301,444\n1717 \u00d7 42 = 72,114\n301,444 \u2212 72,114 = 229,330\n229,330 + 903 = 230,233\n\nAnswer: 230233"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "272208", "out": "7515 \u00d7 66 = 495,990\n3806 \u00d7 59 = 224,554\n495,990 \u2212 224,554 = 271,436\n271,436 + 772 = 272,208\n\nAnswer: 272208"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "69180", "out": "6269 \u00d7 86 = 539,134\n5957 \u00d7 79 = 470,603\n539,134 \u2212 470,603 = 68,531\n68,531 + 649 = 69,180\n\nAnswer: 69180"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "334363", "out": "9677 \u00d7 47 = 454,819\n4179 \u00d7 29 = 121,191\n454,819 \u2212 121,191 = 333,628\n333,628 + 735 = 334,363\n\nAnswer: 334363"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-208826", "out": "8737 \u00d7 38 = 332,006\n9331 \u00d7 58 = 541,198\n332,006 \u2212 541,198 = \u2212209,192\n\u2212209,192 + 366 = \u2212208,826\n\nAnswer: -208826"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "424966", "out": "9390 \u00d7 62 = 582,180\n2876 \u00d7 55 = 158,180\n582,180 \u2212 158,180 = 424,000\n424,000 + 966 = 424,966\n\nAnswer: 424966"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-97123", "out": "4546 \u00d7 62 = 281,852\n8820 \u00d7 43 = 379,260\n281,852 \u2212 379,260 = \u221297,408\n\u221297,408 + 285 = \u221297,123\n\nAnswer: -97123"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "388079", "out": "5602 \u00d7 86 = 481,772\n4503 \u00d7 21 = 94,563\n481,772 \u2212 94,563 = 387,209\n387,209 + 870 = 388,079\n\nAnswer: 388079"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "543005", "out": "9107 \u00d7 75 = 683,025\n4390 \u00d7 32 = 140,480\n683,025 \u2212 140,480 = 542,545\n542,545 + 460 = 543,005\n\nAnswer: 543005"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "146332", "out": "4 \u00d7 80 \u2212 9084 = 726720 \u2212 9084 = 717636\n5957 \u00d7 96 = 5957 \u00d7 100 \u2212 5957 \u00d7 4 = 595700 \u2212 23828 = 571872\n717636 \u2212 571872 + 568 = 145764 + 568 = 146332\n\nAnswer: 146332"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "255979", "out": "7937 \u00d7 49 = 7937 \u00d7 50 \u2212 7937 = 396850 \u2212 7937 = 388913\n1374 \u00d7 97 = 1374 \u00d7 100 \u2212 1374 \u00d7 3 = 137400 \u2212 4122 = 133278\n388913 \u2212 133278 + 344 = 255979\n\nAnswer: 255979"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "164363", "out": "8136 \u00d7 62 = 504,432\n9454 \u00d7 36 = 340,344\n504,432 \u2212 340,344 = 164,088\n164,088 + 275 = 164,363\n\nAnswer: 164363"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "69475", "out": "7304 \u00d7 45 = 328,680\n3325 \u00d7 78 = 259,350\n328,680 \u2212 259,350 = 69,330\n69,330 + 145 = 69,475\n\nAnswer: 69475"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "265189", "out": "9468 \u00d7 55 = 520,740\n2724 \u00d7 94 = 256,056\n520,740 \u2212 256,056 = 264,684\n264,684 + 505 = 265,189\n\nAnswer: 265189"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "201199", "out": "8003 \u00d7 42 = 336,126\n2649 \u00d7 51 = 135,099\n336,126 \u2212 135,099 = 201,027\n201,027 + 172 = 201,199\n\nAnswer: 201199"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "360799", "out": "7206 \u00d7 83 = 598,098\n2676 \u00d7 89 = 238,164\n598,098 \u2212 238,164 = 359,934\n359,934 + 865 = 360,799\n\nAnswer: 360799"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-151125", "out": "2884 \u00d7 78 = 224,952\n5799 \u00d7 65 = 376,935\n224,952 \u2212 376,935 = \u2212151,983\n\u2212151,983 + 858 = \u2212151,125\n\nAnswer: -151125"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "472793", "out": "8288 \u00d7 75 = 621,600\n2823 \u00d7 53 = 149,619\n621,600 \u2212 149,619 = 471,981\n471,981 + 812 = 472,793\n\nAnswer: 472793"} +{"tag": "rc4-on", "kind": "arith", "ok": false, "answer": "-2846", "out": "1193 \u00d7 91 = 108,523\n3018 \u00d7 37 = 111,666\n108,523 \u2212 111,666 = \u22123,143\n\u22123,143 + 257 = \u22122,886\n\nAnswer: -2886"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "760889", "out": "9595 \u00d7 83 = 796,385\n1919 \u00d7 19 = 36,461\n796,385 \u2212 36,461 = 759,924\n759,924 + 965 = 760,889\n\nAnswer: 760889"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "19454", "out": "8296 \u00d7 21 = 174,216\n4699 \u00d7 33 = 155,067\n174,216 \u2212 155,067 = 19,149\n19,149 + 305 = 19,454\n\nAnswer: 19454"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "102142", "out": "5351 \u00d7 57 = 267550 + 37457 = 305007\n8120 \u00d7 25 = 203000\n305007 \u2212 203000 + 135 = 102142\n\nAnswer: 102142"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "354424", "out": "9737 \u00d7 90 = 876,330\n9682 \u00d7 54 = 522,828\n876,330 \u2212 522,828 = 353,502\n353,502 + 922 = 354,424\n\nAnswer: 354424"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "160620", "out": "5488 \u00d7 40 = 219,520\n3152 \u00d7 19 = 59,888\n219,520 \u2212 59,888 = 159,632\n159,632 + 988 = 160,620\n\nAnswer: 160620"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-249458", "out": "3392 \u00d7 13 = 44,096\n5547 \u00d7 53 = 293,991\n44,096 \u2212 293,991 + 437 = \u2212249,458\n\nAnswer: -249458"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-138072", "out": "9911 \u00d7 20 = 198,220\n5806 \u00d7 58 = 336,748\n198,220 \u2212 336,748 + 456 = \u2212138,072\n\nAnswer: -138072"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "19304", "out": "1514 \u00d7 38 = 57,532\n2798 \u00d7 14 = 39,172\n57,532 \u2212 39,172 = 18,360\n18,360 + 944 = 19,304\n\nAnswer: 19304"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-98662", "out": "2276 \u00d7 13 = 29,588\n3386 \u00d7 38 = 128,668\n29,588 \u2212 128,668 + 418 = \u221298,662\n\nAnswer: -98662"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-449406", "out": "1424 \u00d7 34 = 48,416\n7546 \u00d7 66 = 498,036\n48,416 \u2212 498,036 + 214 = \u2212449,406\n\nAnswer: -449406"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "309608", "out": "9594 \u00d7 87 = 834,678\n7407 \u00d7 71 = 525,897\n834,678 \u2212 525,897 = 308,781\n308,781 + 827 = 309,608\n\nAnswer: 309608"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-215693", "out": "9808 \u00d7 48 = 470,784\n9957 \u00d7 69 = 687,033\n470,784 \u2212 687,033 = \u2212216,249\n\u2212216,249 + 556 = \u2212215,693\n\nAnswer: -215693"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "126970", "out": "9613 \u00d7 89 = 9613 \u00d7 90 \u2212 9613 = 865170 \u2212 9613 = 855557\n9720 \u00d7 75 = 729000\n855557 \u2212 729000 + 413 = 126970\n\nAnswer: 126970"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "38771", "out": "2191 \u00d7 60 = 131,460\n2745 \u00d7 34 = 93,330\n131,460 \u2212 93,330 = 38,130\n38,130 + 641 = 38,771\n\nAnswer: 38771"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-743614", "out": "1429 \u00d7 64 = 91,456\n9493 \u00d7 88 = 835,384\n91,456 \u2212 835,384 + 314 = \u2212743,614\n\nAnswer: -743614"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "340236", "out": "5460 \u00d7 93 = 507,780\n2853 \u00d7 59 = 168,327\n507,780 \u2212 168,327 = 339,453\n339,453 + 783 = 340,236\n\nAnswer: 340236"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "106833", "out": "9818 \u00d7 20 = 196,360\n2917 \u00d7 31 = 90,427\n196,360 \u2212 90,427 + 900 = 106,833\n\nAnswer: 106833"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "46981", "out": "9296 \u00d7 25 = 232,400\n2824 \u00d7 66 = 186,384\n232,400 \u2212 186,384 = 46,016\n46,016 + 965 = 46,981\n\nAnswer: 46981"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "700873", "out": "9331 \u00d7 89 = 9331 \u00d7 90 \u2212 9331 = 839,790 \u2212 9,331 = 830,459\n8121 \u00d7 16 = 129,936\n830,459 \u2212 129,936 + 350 = 700,873\n\nAnswer: 700873"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "17440", "out": "2327 \u00d7 44 = 102,388\n2130 \u00d7 40 = 85,200\n102,388 \u2212 85,200 = 17,188\n17,188 + 252 = 17,440\n\nAnswer: 17440"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-98639", "out": "2233 \u00d7 33 = 73,689\n1836 \u00d7 94 = 172,584\n73,689 \u2212 172,584 + 256 = \u221298,639\n\nAnswer: -98639"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-64110", "out": "2713 \u00d7 45 = 122,085\n3063 \u00d7 61 = 186,843\n122,085 \u2212 186,843 = \u221264,758\n\u221264,758 + 648 = \u221264,110\n\nAnswer: -64110"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-157733", "out": "1446 \u00d7 74 = 107,004\n3157 \u00d7 84 = 265,188\n107,004 \u2212 265,188 = \u2212158,184\n\u2212158,184 + 451 = \u2212157,733\n\nAnswer: -157733"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-22155", "out": "6595 \u00d7 18 = 118,710\n2670 \u00d7 53 = 141,510\n118,710 \u2212 141,510 + 645 = \u221222,800 + 645 = \u221222,155\n\nAnswer: -22155"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "77674", "out": "6322 \u00d7 47 = 6322 \u00d7 40 + 6322 \u00d7 7 = 252,880 + 44,254 = 297,134\n8791 \u00d7 25 = 219,775\n297,134 \u2212 219,775 = 77,359\n77,359 + 315 = 77,674\n\nAnswer: 77674"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-181863", "out": "7224 \u00d7 30 = 216,720\n9068 \u00d7 44 = 398,992\n216,720 \u2212 398,992 = \u2212182,272\n\u2212182,272 + 409 = \u2212181,863\n\nAnswer: -181863"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "655369", "out": "8363 \u00d7 86 = 719,218\n4979 \u00d7 13 = 64,727\n719,218 \u2212 64,727 + 878 = 655,369\n\nAnswer: 655369"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-492938", "out": "1577 \u00d7 96 = 151,392\n7090 \u00d7 91 = 645,190\n151,392 \u2212 645,190 + 860 = \u2212492,938\n\nAnswer: -492938"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-102857", "out": "3598 \u00d7 14 = 50,372\n2751 \u00d7 56 = 154,056\n50,372 \u2212 154,056 = \u2212103,684\n\u2212103,684 + 827 = \u2212102,857\n\nAnswer: -102857"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "302260", "out": "89 = 4285 \u00d7 90 \u2212 4285 = 385650 \u2212 4285 = 381365\n1621 \u00d7 49 = 1621 \u00d7 50 \u2212 1621 = 81050 \u2212 1621 = 79429\n381365 \u2212 79429 = 301936\n301936 + 324 = 302260\n\nAnswer: 302260"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-386616", "out": "1521 \u00d7 47 = 71,487\n9547 \u00d7 48 = 458,256\n71,487 \u2212 458,256 + 153 = \u2212386,616\n\nAnswer: -386616"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "91849", "out": "4437 \u00d7 86 = 381,582\n3769 \u00d7 77 = 290,213\n381,582 \u2212 290,213 = 91,369\n91,369 + 480 = 91,849\n\nAnswer: 91849"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "468740", "out": "8388 \u00d7 73 = 612,324\n2121 \u00d7 68 = 144,228\n612,324 \u2212 144,228 = 468,096\n468,096 + 644 = 468,740\n\nAnswer: 468740"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-30267", "out": "9530 \u00d7 50 = 476,500\n6764 \u00d7 75 = 507,300\n476,500 \u2212 507,300 = \u221230,800\n\u221230,800 + 533 = \u221230,267\n\nAnswer: -30267"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-581995", "out": "4968 \u00d7 24 = 119,232\n7973 \u00d7 88 = 701,624\n119,232 \u2212 701,624 + 397 = \u2212581,995\n\nAnswer: -581995"} +{"tag": "rc4-on", "kind": "arith", "ok": false, "answer": "189476", "out": " = 3908 \u00d7 90 \u2212 3908 = 351720 \u2212 3908 = 347812\n5124 \u00d7 31 = 5124 \u00d7 30 + 5124 = 153720 + 5124 = 158844\n347812 \u2212 158844 + 508 = 189468 + 508 = 189976\n\nAnswer: 189976"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-90211", "out": "1745 \u00d7 30 = 52,350\n4344 \u00d7 33 = 143,352\n52,350 \u2212 143,352 = \u221291,002\n\u221291,002 + 791 = \u221290,211\n\nAnswer: -90211"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-210835", "out": "1329 \u00d7 25 = 33,225\n5693 \u00d7 43 = 244,799\n33,225 \u2212 244,799 = \u2212211,574\n\u2212211,574 + 739 = \u2212210,835\n\nAnswer: -210835"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "542193", "out": "8556 \u00d7 77 = 658,812\n2057 \u00d7 57 = 117,249\n658,812 \u2212 117,249 = 541,563\n541,563 + 630 = 542,193\n\nAnswer: 542193"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-133948", "out": "9213 \u00d7 51 = 469,863\n6943 \u00d7 87 = 604,041\n469,863 \u2212 604,041 = \u2212134,178\n\u2212134,178 + 230 = \u2212133,948\n\nAnswer: -133948"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "13475", "out": "5684 \u00d7 21 = 119,364\n1538 \u00d7 69 = 106,122\n119,364 \u2212 106,122 = 13,242\n13,242 + 233 = 13,475\n\nAnswer: 13475"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-348585", "out": "2362 \u00d7 32 = 75,584\n9444 \u00d7 45 = 424,980\n75,584 \u2212 424,980 = \u2212349,396\n\u2212349,396 + 811 = \u2212348,585\n\nAnswer: -348585"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "130069", "out": "4025 \u00d7 73 = 293,825\n6329 \u00d7 26 = 164,554\n293,825 \u2212 164,554 = 129,271\n129,271 + 798 = 130,069\n\nAnswer: 130069"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "61744", "out": "6867 \u00d7 88 = 604,296\n9204 \u00d7 59 = 543,036\n604,296 \u2212 543,036 = 61,260\n61,260 + 484 = 61,744\n\nAnswer: 61744"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-645458", "out": "2102 \u00d7 25 = 52,550\n9440 \u00d7 74 = 698,560\n52,550 \u2212 698,560 + 552 = \u2212645,458\n\nAnswer: -645458"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-249431", "out": "3878 \u00d7 29 = 112,462\n7887 \u00d7 46 = 362,802\n112,462 \u2212 362,802 + 909 = \u2212249,431\n\nAnswer: -249431"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-166098", "out": "1888 \u00d7 62 = 117,056\n4886 \u00d7 58 = 283,388\n117,056 \u2212 283,388 = \u2212166,332\n\u2212166,332 + 234 = \u2212166,098\n\nAnswer: -166098"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "602804", "out": "7560 \u00d7 85 = 642,600\n2366 \u00d7 17 = 40,222\n642,600 \u2212 40,222 = 602,378\n602,378 + 426 = 602,804\n\nAnswer: 602804"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-186087", "out": "1142 \u00d7 19 = 21698\n7438 \u00d7 28 = 208264\n21698 \u2212 208264 + 479 = 22177 \u2212 208264 = \u2212186087\n\nAnswer: -186087"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-378261", "out": "5550 \u00d7 16 = 88,800\n5312 \u00d7 88 = 467,456\n88,800 \u2212 467,456 + 395 = \u2212378,261\n\nAnswer: -378261"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "266720", "out": "6352 \u00d7 90 = 571,680\n7106 \u00d7 43 = 305,558\n571,680 \u2212 305,558 = 266,122\n266,122 + 598 = 266,720\n\nAnswer: 266720"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "118843", "out": "7058 \u00d7 94 = 663,452\n9904 \u00d7 55 = 544,720\n663,452 \u2212 544,720 = 118,732\n118,732 + 111 = 118,843\n\nAnswer: 118843"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "529378", "out": "9 \u00d7 100 \u2212 8839 \u00d7 3 = 883900 \u2212 26517 = 857383\n3690 \u00d7 89 = 3690 \u00d7 90 \u2212 3690 = 332100 \u2212 3690 = 328410\n857383 \u2212 328410 + 405 = 528973 + 405 = 529378\n\nAnswer: 529378"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "60634", "out": "2421 \u00d7 79 = 191,259\n2342 \u00d7 56 = 131,152\n191,259 \u2212 131,152 = 60,107\n60,107 + 527 = 60,634\n\nAnswer: 60634"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "26228", "out": "1952 \u00d7 74 = 144,448\n9148 \u00d7 13 = 118,924\n144,448 \u2212 118,924 = 25,524\n25,524 + 704 = 26,228\n\nAnswer: 26228"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "523248", "out": "8713 \u00d7 94 = 819,022\n3565 \u00d7 83 = 295,895\n819,022 \u2212 295,895 = 523,127\n523,127 + 121 = 523,248\n\nAnswer: 523248"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-189839", "out": "2615 \u00d7 92 = 240,580\n7060 \u00d7 61 = 430,660\n240,580 \u2212 430,660 = \u2212190,080\n\u2212190,080 + 241 = \u2212189,839\n\nAnswer: -189839"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-74851", "out": "1936 \u00d7 27 = 52,272\n2062 \u00d7 62 = 127,844\n52,272 \u2212 127,844 + 721 = \u221274,851\n\nAnswer: -74851"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "147458", "out": "4876 \u00d7 44 = 214,544\n1052 \u00d7 64 = 67,328\n214,544 \u2212 67,328 = 147,216\n147,216 + 242 = 147,458\n\nAnswer: 147458"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-330683", "out": "3139 \u00d7 40 = 125,560\n9131 \u00d7 50 = 456,550\n125,560 \u2212 456,550 = \u2212330,990\n\u2212330,990 + 307 = \u2212330,683\n\nAnswer: -330683"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-163481", "out": "6117 \u00d7 45 = 275,265\n5225 \u00d7 84 = 438,900\n275,265 \u2212 438,900 = \u2212163,635\n\u2212163,635 + 154 = \u2212163,481\n\nAnswer: -163481"} +{"tag": "rc4-on", "kind": "arith", "ok": false, "answer": "-522555", "out": "2611 \u00d7 71 = 185,381\n9451 \u00d7 75 = 708,825\n185,381 \u2212 708,825 + 889 = \u2212523,555\n\nAnswer: -523555"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "162324", "out": "9033 \u00d7 79 = 713,607\n7886 \u00d7 70 = 552,020\n713,607 \u2212 552,020 = 161,587\n161,587 + 737 = 162,324\n\nAnswer: 162324"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-234827", "out": "2763 \u00d7 28 = 77,364\n4526 \u00d7 69 = 312,294\n77,364 \u2212 312,294 = \u2212234,930\n\u2212234,930 + 103 = \u2212234,827\n\nAnswer: -234827"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-73648", "out": "2794 \u00d7 64 = 178,816\n2605 \u00d7 97 = 252,685\n178,816 \u2212 252,685 = \u221273,869\n\u221273,869 + 221 = \u221273,648\n\nAnswer: -73648"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "129170", "out": "8871 \u00d7 27 = 239,517\n7414 \u00d7 15 = 111,210\n239,517 \u2212 111,210 = 128,307\n128,307 + 863 = 129,170\n\nAnswer: 129170"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-96675", "out": "4521 \u00d7 19 = 85,899\n7050 \u00d7 26 = 183,300\n85,899 \u2212 183,300 + 726 = \u221296,675\n\nAnswer: -96675"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-321242", "out": "1360 \u00d7 62 = 84,320\n8643 \u00d7 47 = 406,221\n84,320 \u2212 406,221 + 659 = \u2212321,242\n\nAnswer: -321242"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "697618", "out": "9110 \u00d7 97 = 9110 \u00d7 100 \u2212 9110 \u00d7 3 = 911000 \u2212 27330 = 883670\n7455 \u00d7 25 = 186375\n883670 \u2212 186375 + 323 = 697295 + 323 = 697618\n\nAnswer: 697618"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "8031", "out": "3839 \u00d7 97 = 3839 \u00d7 100 \u2212 3839 \u00d7 3 = 383900 \u2212 11517 = 372383\n9130 \u00d7 40 = 365200\n372383 \u2212 365200 = 7183\n7183 + 848 = 8031\n\nAnswer: 8031"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-271800", "out": "3148 \u00d7 29 = 91,292\n5688 \u00d7 64 = 364,032\n91,292 \u2212 364,032 + 940 = \u2212271,800\n\nAnswer: -271800"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "163568", "out": "5058 \u00d7 96 = 485,568\n7005 \u00d7 46 = 322,230\n485,568 \u2212 322,230 = 163,338\n163,338 + 230 = 163,568\n\nAnswer: 163568"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "55448", "out": "8651 \u00d7 85 = 735,335\n7478 \u00d7 91 = 680,498\n735,335 \u2212 680,498 = 54,837\n54,837 + 611 = 55,448\n\nAnswer: 55448"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "246262", "out": "6401 \u00d7 66 = 422,466\n2385 \u00d7 74 = 176,490\n422,466 \u2212 176,490 = 245,976\n245,976 + 286 = 246,262\n\nAnswer: 246262"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-200227", "out": "3570 \u00d7 73 = 260,610\n8235 \u00d7 56 = 461,160\n260,610 \u2212 461,160 = \u2212200,550\n\u2212200,550 + 323 = \u2212200,227\n\nAnswer: -200227"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-390312", "out": "6882 \u00d7 40 = 275,280\n8649 \u00d7 77 = 605,430 + 60,543 = 665,973\n275,280 \u2212 665,973 = \u2212390,693\n\u2212390,693 + 381 = \u2212390,312\n\nAnswer: -390312"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-32986", "out": "5838 \u00d7 14 = 81,732\n8880 \u00d7 13 = 115,440\n81,732 \u2212 115,440 = \u221233,708\n\u221233,708 + 722 = \u221232,986\n\nAnswer: -32986"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-320293", "out": "2933 \u00d7 81 = 237,573\n5757 \u00d7 97 = 558,429\n237,573 \u2212 558,429 = \u2212320,856\n\u2212320,856 + 563 = \u2212320,293\n\nAnswer: -320293"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "386032", "out": "7001 \u00d7 83 = 581,083\n8907 \u00d7 22 = 195,954\n581,083 \u2212 195,954 = 385,129\n385,129 + 903 = 386,032\n\nAnswer: 386032"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-65084", "out": "4666 \u00d7 33 = 153,978\n5358 \u00d7 41 = 219,678\n153,978 \u2212 219,678 = \u221265,700\n\u221265,700 + 616 = \u221265,084\n\nAnswer: -65084"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-386258", "out": "7220 \u00d7 25 = 180,500\n9775 \u00d7 58 = 566,950\n180,500 \u2212 566,950 = \u2212386,450\n\u2212386,450 + 192 = \u2212386,258\n\nAnswer: -386258"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "129503", "out": "3489 \u00d7 77 = 268,653\n3171 \u00d7 44 = 139,524\n268,653 \u2212 139,524 = 129,129\n129,129 + 374 = 129,503\n\nAnswer: 129503"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "51187", "out": "3194 \u00d7 51 = 162,894\n1348 \u00d7 83 = 111,884\n162,894 \u2212 111,884 = 51,010\n51,010 + 177 = 51,187\n\nAnswer: 51187"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "134975", "out": "7416 \u00d7 57 = 422,712\n5647 \u00d7 51 = 287,997\n422,712 \u2212 287,997 = 134,715\n134,715 + 260 = 134,975\n\nAnswer: 134975"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "25983", "out": "6592 \u00d7 23 = 151,616\n1658 \u00d7 76 = 126,008\n151,616 \u2212 126,008 = 25,608\n25,608 + 375 = 25,983\n\nAnswer: 25983"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-434096", "out": "5521 \u00d7 22 = 121,462\n7943 \u00d7 70 = 556,010\n121,462 \u2212 556,010 = \u2212434,548\n\u2212434,548 + 452 = \u2212434,096\n\nAnswer: -434096"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-66683", "out": "3870 \u00d7 39 = 150,930\n9101 \u00d7 24 = 218,424\n150,930 \u2212 218,424 = \u221267,494\n\u221267,494 + 811 = \u221266,683\n\nAnswer: -66683"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "199878", "out": "4823 \u00d7 93 = 448,539\n6232 \u00d7 40 = 249,280\n448,539 \u2212 249,280 = 199,259\n199,259 + 619 = 199,878\n\nAnswer: 199878"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "419543", "out": "8101 \u00d7 80 = 648,080\n9563 \u00d7 24 = 229,512\n648,080 \u2212 229,512 = 418,568\n418,568 + 975 = 419,543\n\nAnswer: 419543"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "211136", "out": "7280 \u00d7 46 = 334,880\n1946 \u00d7 64 = 124,544\n334,880 \u2212 124,544 = 210,336\n210,336 + 800 = 211,136\n\nAnswer: 211136"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-12536", "out": "2292 \u00d7 34 = 77,928\n3780 \u00d7 24 = 90,720\n77,928 \u2212 90,720 + 256 = \u221212,792 + 256 = \u221212,536\n\nAnswer: -12536"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-223112", "out": "1611 \u00d7 54 = 86,994\n6345 \u00d7 49 = 310,905\n86,994 \u2212 310,905 + 799 = \u2212223,112\n\nAnswer: -223112"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "531054", "out": "9826 \u00d7 97 = 953,122\n7161 \u00d7 59 = 422,499\n953,122 \u2212 422,499 = 530,623\n530,623 + 431 = 531,054\n\nAnswer: 531054"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "116040", "out": "6472 \u00d7 72 = 465,984\n7004 \u00d7 50 = 350,200\n465,984 \u2212 350,200 = 115,784\n115,784 + 256 = 116,040\n\nAnswer: 116040"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-252220", "out": "9399 \u00d7 27 = 253,773\n5823 \u00d7 87 = 506,601\n253,773 \u2212 506,601 = \u2212252,828\n\u2212252,828 + 608 = \u2212252,220\n\nAnswer: -252220"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-213343", "out": "1746 \u00d7 43 = 75,078\n4982 \u00d7 58 = 288,956\n75,078 \u2212 288,956 = \u2212213,878\n\u2212213,878 + 535 = \u2212213,343\n\nAnswer: -213343"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-245844", "out": "2175 \u00d7 59 = 128,325\n3983 \u00d7 94 = 374,402\n128,325 \u2212 374,402 + 233 = \u2212245,844\n\nAnswer: -245844"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-203808", "out": "8431 \u00d7 34 = 286,654\n8465 \u00d7 58 = 490,970\n286,654 \u2212 490,970 = \u2212204,316\n\u2212204,316 + 508 = \u2212203,808\n\nAnswer: -203808"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-365490", "out": "3490 \u00d7 63 = 219,870\n8883 \u00d7 66 = 586,278\n219,870 \u2212 586,278 + 918 = \u2212365,490\n\nAnswer: -365490"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "63551", "out": "6892 \u00d7 28 = 192,976\n2763 \u00d7 47 = 129,861\n192,976 \u2212 129,861 = 63,115\n63,115 + 436 = 63,551\n\nAnswer: 63551"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "14320", "out": "4441 \u00d7 94 = 417,454\n5045 \u00d7 80 = 403,600\n417,454 \u2212 403,600 = 13,854\n13,854 + 466 = 14,320\n\nAnswer: 14320"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "241245", "out": "6696 \u00d7 71 = 475,416\n3358 \u00d7 70 = 235,060\n475,416 \u2212 235,060 = 240,356\n240,356 + 889 = 241,245\n\nAnswer: 241245"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-560058", "out": "4971 \u00d7 23 = 114,333\n9368 \u00d7 72 = 674,496\n114,333 \u2212 674,496 + 105 = \u2212560,058\n\nAnswer: -560058"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-65554", "out": "5374 \u00d7 75 = 403,050\n6802 \u00d7 69 = 469,338\n403,050 \u2212 469,338 = \u221266,288\n\u221266,288 + 734 = \u221265,554\n\nAnswer: -65554"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-418069", "out": "6346 \u00d7 30 = 190,380\n9522 \u00d7 64 = 609,408\n190,380 \u2212 609,408 = \u2212419,028\n\u2212419,028 + 959 = \u2212418,069\n\nAnswer: -418069"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "282189", "out": "3659 \u00d7 83 = 3659 \u00d7 80 + 3659 \u00d7 3 = 292,720 + 10,977 = 303,697\n1357 \u00d7 16 = 21,712\n303,697 \u2212 21,712 = 281,985\n281,985 + 204 = 282,189\n\nAnswer: 282189"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-198499", "out": "8718 \u00d7 53 = 462,054\n9317 \u00d7 71 = 661,507\n462,054 \u2212 661,507 = \u2212199,453\n\u2212199,453 + 954 = \u2212198,499\n\nAnswer: -198499"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-114903", "out": "1317 \u00d7 90 = 118,530\n6510 \u00d7 36 = 234,360\n118,530 \u2212 234,360 = \u2212115,830\n\u2212115,830 + 927 = \u2212114,903\n\nAnswer: -114903"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-136060", "out": "4317 \u00d7 40 = 172,680\n3725 \u00d7 83 = 3725 \u00d7 80 + 3725 \u00d7 3 = 298,000 + 11,175 = 309,175\n172,680 \u2212 309,175 + 435 = \u2212136,495 + 435 = \u2212136,060\n\nAnswer: -136060"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "181134", "out": "5418 \u00d7 42 = 227,556\n1605 \u00d7 29 = 46,545\n227,556 \u2212 46,545 = 181,011\n181,011 + 123 = 181,134\n\nAnswer: 181134"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-167329", "out": "2631 \u00d7 36 = 94,716\n3857 \u00d7 68 = 262,276\n94,716 \u2212 262,276 + 231 = \u2212167,329\n\nAnswer: -167329"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-133549", "out": "5790 \u00d7 50 = 289,500\n8639 \u00d7 49 = 8639 \u00d7 50 \u2212 8639 = 431,950 \u2212 8,639 = 423,311\n289,500 \u2212 423,311 + 262 = \u2212133,549\n\nAnswer: -133549"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "298191", "out": "6991 \u00d7 49 = 6991 \u00d7 50 \u2212 6991 = 349,550 \u2212 6,991 = 342,559\n3006 \u00d7 15 = 45,090\n342,559 \u2212 45,090 + 722 = 297,469 + 722 = 298,191\n\nAnswer: 298191"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "263933", "out": "6125 \u00d7 69 = 422,625\n2381 \u00d7 67 = 159,527\n422,625 \u2212 159,527 = 263,098\n263,098 + 835 = 263,933\n\nAnswer: 263933"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "421218", "out": "6667 \u00d7 76 = 506,692\n1208 \u00d7 71 = 85,768\n506,692 \u2212 85,768 = 420,924\n420,924 + 294 = 421,218\n\nAnswer: 421218"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-286259", "out": "1469 \u00d7 60 = 88,140\n9156 \u00d7 41 = 375,396\n88,140 \u2212 375,396 + 997 = \u2212286,259\n\nAnswer: -286259"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "205549", "out": "8453 \u00d7 68 = 574,804\n8796 \u00d7 42 = 369,432\n574,804 \u2212 369,432 = 205,372\n205,372 + 177 = 205,549\n\nAnswer: 205549"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "213419", "out": "5146 \u00d7 74 = 380,804\n1995 \u00d7 84 = 167,580\n380,804 \u2212 167,580 = 213,224\n213,224 + 195 = 213,419\n\nAnswer: 213419"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "126694", "out": "5595 \u00d7 74 = 414,030\n3028 \u00d7 95 = 287,660\n414,030 \u2212 287,660 = 126,370\n126,370 + 324 = 126,694\n\nAnswer: 126694"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-575727", "out": "4220 \u00d7 41 = 173,020\n8710 \u00d7 86 = 749,060\n173,020 \u2212 749,060 = \u2212576,040\n\u2212576,040 + 313 = \u2212575,727\n\nAnswer: -575727"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-607843", "out": "9602 \u00d7 28 = 268,856\n9861 \u00d7 89 = 877,629\n268,856 \u2212 877,629 + 930 = \u2212607,843\n\nAnswer: -607843"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-78971", "out": "9764 \u00d7 13 = 126,932\n4922 \u00d7 42 = 206,724\n126,932 \u2212 206,724 = \u221279,792\n\u221279,792 + 821 = \u221278,971\n\nAnswer: -78971"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-81441", "out": "7714 \u00d7 25 = 192,850\n3669 \u00d7 75 = 275,175\n192,850 \u2212 275,175 = \u221282,325\n\u221282,325 + 884 = \u221281,441\n\nAnswer: -81441"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-41191", "out": "9751 \u00d7 27 = 263,277\n3808 \u00d7 80 = 304,640\n263,277 \u2212 304,640 + 172 = \u221241,191\n\nAnswer: -41191"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-545573", "out": "8628 \u00d7 16 = 138,048\n7957 \u00d7 86 = 684,302\n138,048 \u2212 684,302 + 681 = \u2212545,573\n\nAnswer: -545573"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-111221", "out": "6074 \u00d7 14 = 85036\n3516 \u00d7 56 = 196896\n85036 \u2212 196896 + 639 = \u2212111221\n\nAnswer: -111221"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "278514", "out": "8852 \u00d7 73 = 646,196\n9679 \u00d7 38 = 367,802\n646,196 \u2212 367,802 + 120 = 278,514\n\nAnswer: 278514"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "350267", "out": "8811 \u00d7 62 = 546,282\n5616 \u00d7 35 = 196,560\n546,282 \u2212 196,560 = 349,722\n349,722 + 545 = 350,267\n\nAnswer: 350267"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-150805", "out": "3994 \u00d7 73 = 291,562\n8051 \u00d7 55 = 442,805\n291,562 \u2212 442,805 = \u2212151,243\n\u2212151,243 + 438 = \u2212150,805\n\nAnswer: -150805"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "798424", "out": "9171 \u00d7 93 = 852,903\n1045 \u00d7 53 = 55,385\n852,903 \u2212 55,385 = 797,518\n797,518 + 906 = 798,424\n\nAnswer: 798424"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-231965", "out": "1689 \u00d7 85 = 143,565\n6711 \u00d7 56 = 375,816\n143,565 \u2212 375,816 = \u2212232,251\n\u2212232,251 + 286 = \u2212231,965\n\nAnswer: -231965"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-23106", "out": "2497 \u00d7 92 = 229,724\n3164 \u00d7 80 = 253,120\n229,724 \u2212 253,120 = \u221223,396\n\u221223,396 + 290 = \u221223,106\n\nAnswer: -23106"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "89614", "out": "7943 \u00d7 43 = 341,549\n3832 \u00d7 66 = 252,912\n341,549 \u2212 252,912 = 88,637\n88,637 + 977 = 89,614\n\nAnswer: 89614"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "102850", "out": "6769 \u00d7 60 = 406,140\n7407 \u00d7 41 = 303,687\n406,140 \u2212 303,687 = 102,453\n102,453 + 397 = 102,850\n\nAnswer: 102850"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-79615", "out": "7510 \u00d7 68 = 510,680\n8824 \u00d7 67 = 591,208\n510,680 \u2212 591,208 = \u221280,528\n\u221280,528 + 913 = \u221279,615\n\nAnswer: -79615"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "312660", "out": "6177 \u00d7 58 = 358,266\n2862 \u00d7 16 = 45,792\n358,266 \u2212 45,792 + 186 = 312,660\n\nAnswer: 312660"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "195510", "out": "9519 \u00d7 36 = 342,684\n3274 \u00d7 45 = 147,330\n342,684 \u2212 147,330 = 195,354\n195,354 + 156 = 195,510\n\nAnswer: 195510"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-103198", "out": "1219 \u00d7 69 = 84,111\n9865 \u00d7 19 = 187,435\n84,111 \u2212 187,435 + 126 = \u2212103,198\n\nAnswer: -103198"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-314255", "out": "3241 \u00d7 41 = 132,881\n7993 \u00d7 56 = 447,608\n132,881 \u2212 447,608 + 472 = \u2212314,255\n\nAnswer: -314255"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-426516", "out": "2557 \u00d7 87 = 222,459\n8774 \u00d7 74 = 649,276\n222,459 \u2212 649,276 + 301 = \u2212426,516\n\nAnswer: -426516"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-309687", "out": "6381 \u00d7 66 = 421,146\n7949 \u00d7 92 = 731,308\n421,146 \u2212 731,308 = \u2212310,162\n\u2212310,162 + 475 = \u2212309,687\n\nAnswer: -309687"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "26842", "out": "6477 \u00d7 22 = 142,494\n1261 \u00d7 92 = 116,012\n142,494 \u2212 116,012 = 26,482\n26,482 + 360 = 26,842\n\nAnswer: 26842"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "612111", "out": "258 \u00d7 100 \u2212 7258 \u00d7 3 = 725800 \u2212 21774 = 704026\n3188 \u00d7 29 = 3188 \u00d7 30 \u2212 3188 = 95640 \u2212 3188 = 92452\n704026 \u2212 92452 = 611574\n611574 + 537 = 612111\n\nAnswer: 612111"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "178604", "out": "5633 \u00d7 62 = 349,246\n4081 \u00d7 42 = 171,402\n349,246 \u2212 171,402 = 177,844\n177,844 + 760 = 178,604\n\nAnswer: 178604"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "330240", "out": "5660 \u00d7 89 = 503,740\n3352 \u00d7 52 = 174,304\n503,740 \u2212 174,304 = 329,436\n329,436 + 804 = 330,240\n\nAnswer: 330240"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-319260", "out": "5485 \u00d7 20 = 109,700\n8593 \u00d7 50 = 429,650\n109,700 \u2212 429,650 = \u2212319,950\n\u2212319,950 + 690 = \u2212319,260\n\nAnswer: -319260"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "57587", "out": "2768 \u00d7 75 = 207600\n2949 \u00d7 51 = 150399\n207600 \u2212 150399 = 57201\n57201 + 386 = 57587\n\nAnswer: 57587"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-147837", "out": "4358 \u00d7 28 = 122,024\n3561 \u00d7 76 = 270,636\n122,024 \u2212 270,636 = \u2212148,612\n\u2212148,612 + 775 = \u2212147,837\n\nAnswer: -147837"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-510433", "out": "5267 \u00d7 13 = 68471\n8651 \u00d7 67 = 579617\n68471 \u2212 579617 + 713 = \u2212510433\n\nAnswer: -510433"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-209213", "out": "5863 \u00d7 25 = 146,575\n3749 \u00d7 95 = 3749 \u00d7 100 \u2212 3749 \u00d7 5 = 374,900 \u2212 18,745 = 356,155\n146,575 \u2212 356,155 = \u2212209,580\n\u2212209,580 + 367 = \u2212209,213\n\nAnswer: -209213"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-110911", "out": "2842 \u00d7 22 = 62,524\n6446 \u00d7 27 = 174,042\n62,524 \u2212 174,042 = \u2212111,518\n\u2212111,518 + 607 = \u2212110,911\n\nAnswer: -110911"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-316331", "out": "6260 \u00d7 53 = 331,780\n9136 \u00d7 71 = 648,656\n331,780 \u2212 648,656 = \u2212316,876\n\u2212316,876 + 545 = \u2212316,331\n\nAnswer: -316331"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-546283", "out": "5344 \u00d7 16 = 85,504\n9731 \u00d7 65 = 632,515\n85,504 \u2212 632,515 + 728 = \u2212546,283\n\nAnswer: -546283"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "272304", "out": "5586 \u00d7 75 = 418,950\n2726 \u00d7 54 = 147,204\n418,950 \u2212 147,204 = 271,746\n271,746 + 558 = 272,304\n\nAnswer: 272304"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-335177", "out": "1836 \u00d7 20 = 36,720\n5247 \u00d7 71 = 372,537\n36,720 \u2212 372,537 + 640 = \u2212335,177\n\nAnswer: -335177"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-189667", "out": "4036 \u00d7 23 = 92,828\n5439 \u00d7 52 = 282,828\n92,828 \u2212 282,828 + 333 = \u2212190,000 + 333 = \u2212189,667\n\nAnswer: -189667"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "60979", "out": "1241 \u00d7 74 = 91,834\n1668 \u00d7 19 = 31,692\n91,834 \u2212 31,692 = 60,142\n60,142 + 837 = 60,979\n\nAnswer: 60979"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "211693", "out": "7514 \u00d7 90 = 676,260\n7631 \u00d7 61 = 465,491\n676,260 \u2212 465,491 = 210,769\n210,769 + 924 = 211,693\n\nAnswer: 211693"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "460998", "out": "Factor out 69: (7871 \u2212 1197) \u00d7 69 + 492 = 6674 \u00d7 69 + 492\n\n6674 \u00d7 69 = 460,506\n\n460,506 + 492 = 460,998\n\nAnswer: 460998"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-270301", "out": "3369 \u00d7 58 = 195,402\n4960 \u00d7 94 = 466,240\n195,402 \u2212 466,240 = \u2212270,838\n\u2212270,838 + 537 = \u2212270,301\n\nAnswer: -270301"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "195120", "out": "7936 \u00d7 36 = 285,696\n5368 \u00d7 17 = 91,256\n285,696 \u2212 91,256 = 194,440\n194,440 + 680 = 195,120\n\nAnswer: 195120"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-706361", "out": "2494 \u00d7 25 = 62,350\n9864 \u00d7 78 = 769,392\n62,350 \u2212 769,392 + 681 = \u2212706,361\n\nAnswer: -706361"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "151029", "out": "5313 \u00d7 71 = 377,223\n4631 \u00d7 49 = 226,919\n377,223 \u2212 226,919 = 150,304\n150,304 + 725 = 151,029\n\nAnswer: 151029"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-692775", "out": "Step 1: 1917 \u00d7 40 = 76,680\nStep 2: 8109 \u00d7 95 = 770,355\nStep 3: 76,680 \u2212 770,355 + 900 = \u2212692,775\n\nAnswer: -692775"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-123535", "out": "3294 \u00d7 69 = 227,286\n8174 \u00d7 43 = 351,482\n227,286 \u2212 351,482 = \u2212124,196\n\u2212124,196 + 661 = \u2212123,535\n\nAnswer: -123535"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-132483", "out": "8182 \u00d7 79 = 646,378\n9987 \u00d7 78 = 778,986\n646,378 \u2212 778,986 + 125 = \u2212132,483\n\nAnswer: -132483"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-31284", "out": "7271 \u00d7 36 = 261,756\n6670 \u00d7 44 = 293,480\n261,756 \u2212 293,480 = \u221231,724\n\u221231,724 + 440 = \u221231,284\n\nAnswer: -31284"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-181962", "out": "8215 \u00d7 18 = 147,870\n4286 \u00d7 77 = 4286 \u00d7 70 + 4286 \u00d7 7 = 300,020 + 30,002 = 330,022\n147,870 \u2212 330,022 + 190 = \u2212181,962\n\nAnswer: -181962"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-35", "out": "2524 \u00d7 34 = 85816\n5067 \u00d7 17 = 86139\n85816 \u2212 86139 + 288 = \u2212323 + 288 = \u221235\n\nAnswer: -35"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-78021", "out": "7280 \u00d7 71 = 516,880\n8627 \u00d7 69 = 595,263\n516,880 \u2212 595,263 = \u221278,383\n\u221278,383 + 362 = \u221278,021\n\nAnswer: -78021"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "210104", "out": "00 \u2212 4356 \u00d7 3 = 435600 \u2212 13068 = 422532\n3943 \u00d7 54 = 3943 \u00d7 50 + 3943 \u00d7 4 = 197150 + 15772 = 212922\n422532 \u2212 212922 + 494 = 209610 + 494 = 210104\n\nAnswer: 210104"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "27772", "out": "3616 \u00d7 53 = 191,648\n2196 \u00d7 75 = 164,700\n191,648 \u2212 164,700 = 26,948\n26,948 + 824 = 27,772\n\nAnswer: 27772"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "124369", "out": "4706 \u00d7 38 = 178,828\n1383 \u00d7 40 = 55,320\n178,828 \u2212 55,320 = 123,508\n123,508 + 861 = 124,369\n\nAnswer: 124369"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "165437", "out": "7717 \u00d7 26 = 200,642\n1964 \u00d7 18 = 35,352\n200,642 \u2212 35,352 = 165,290\n165,290 + 147 = 165,437\n\nAnswer: 165437"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "147526", "out": "4470 \u00d7 62 = 277,140\n6214 \u00d7 21 = 130,494\n277,140 \u2212 130,494 = 146,646\n146,646 + 880 = 147,526\n\nAnswer: 147526"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-471586", "out": "2799 \u00d7 61 = 170,739\n6835 \u00d7 94 = 642,490\n170,739 \u2212 642,490 + 165 = \u2212471,586\n\nAnswer: -471586"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-113804", "out": "2750 \u00d7 38 = 104,500\n6262 \u00d7 35 = 219,170\n104,500 \u2212 219,170 + 866 = \u2212113,804\n\nAnswer: -113804"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "9672", "out": "1588 \u00d7 94 = 149,272\n1630 \u00d7 86 = 140,180\n149,272 \u2212 140,180 = 9,092\n9,092 + 580 = 9,672\n\nAnswer: 9672"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "60119", "out": "7822 \u00d7 30 = 234,660\n2015 \u00d7 87 = 175,305\n234,660 \u2212 175,305 = 59,355\n59,355 + 764 = 60,119\n\nAnswer: 60119"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-383971", "out": "1847 \u00d7 62 = 114,514\n5485 \u00d7 91 = 499,135\n114,514 \u2212 499,135 + 650 = \u2212383,971\n\nAnswer: -383971"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "448308", "out": "5562 \u00d7 92 = 511,704\n1782 \u00d7 36 = 64,152\n511,704 \u2212 64,152 = 447,552\n447,552 + 756 = 448,308\n\nAnswer: 448308"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "603710", "out": "7549 \u00d7 88 = 664,312\n4384 \u00d7 14 = 61,376\n664,312 \u2212 61,376 = 602,936\n602,936 + 774 = 603,710\n\nAnswer: 603710"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "282303", "out": "5399 \u00d7 60 = 323,940\n1214 \u00d7 35 = 42,490\n323,940 \u2212 42,490 = 281,450\n281,450 + 853 = 282,303\n\nAnswer: 282303"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "338809", "out": "9993 \u00d7 38 = 379,734\n1332 \u00d7 31 = 41,292\n379,734 \u2212 41,292 = 338,442\n338,442 + 367 = 338,809\n\nAnswer: 338809"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "37753", "out": "2330 \u00d7 70 = 163,100\n3321 \u00d7 38 = 126,198\n163,100 \u2212 126,198 = 36,902\n36,902 + 851 = 37,753\n\nAnswer: 37753"} +{"tag": "rc4-on", "kind": "arith", "ok": false, "answer": "-482711", "out": "1096 \u00d7 52 = 56,992\n7719 \u00d7 70 = 540,330\n56,992 \u2212 540,330 + 627 = \u2212483,711\n\nAnswer: -483711"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "119683", "out": "9403 \u00d7 35 = 329,105\n3564 \u00d7 59 = 210,276\n329,105 \u2212 210,276 = 118,829\n118,829 + 854 = 119,683\n\nAnswer: 119683"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-104908", "out": "2197 \u00d7 17 = 37,349\n2162 \u00d7 66 = 142,692\n37,349 \u2212 142,692 = \u2212105,343\n\u2212105,343 + 435 = \u2212104,908\n\nAnswer: -104908"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-362695", "out": "2519 \u00d7 48 = 120,912\n7123 \u00d7 68 = 484,364\n120,912 \u2212 484,364 = \u2212363,452\n\u2212363,452 + 757 = \u2212362,695\n\nAnswer: -362695"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "54418", "out": "7603 \u00d7 57 = 433,371\n7021 \u00d7 54 = 379,134\n433,371 \u2212 379,134 = 54,237\n54,237 + 181 = 54,418\n\nAnswer: 54418"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "254547", "out": "7115 \u00d7 66 = 469,590\n7709 \u00d7 28 = 215,852\n469,590 \u2212 215,852 = 253,738\n253,738 + 809 = 254,547\n\nAnswer: 254547"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-369437", "out": "1442 \u00d7 13 = 18,746\n4176 \u00d7 93 = 388,368\n18,746 \u2212 388,368 + 185 = \u2212369,437\n\nAnswer: -369437"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "15978", "out": "1013 \u00d7 59 = 59,767\n1139 \u00d7 39 = 44,421\n59,767 \u2212 44,421 = 15,346\n15,346 + 632 = 15,978\n\nAnswer: 15978"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "86049", "out": "4306 \u00d7 73 = 314,338\n2830 \u00d7 81 = 229,230\n314,338 \u2212 229,230 = 85,108\n85,108 + 941 = 86,049\n\nAnswer: 86049"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "25832", "out": "3621 \u00d7 25 = 90,525\n2717 \u00d7 24 = 65,208\n90,525 \u2212 65,208 = 25,317\n25,317 + 515 = 25,832\n\nAnswer: 25832"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-94793", "out": "1595 \u00d7 53 = 84,535\n8997 \u00d7 20 = 179,940\n84,535 \u2212 179,940 + 612 = \u221295,405 + 612 = \u221294,793\n\nAnswer: -94793"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "443100", "out": "6396 \u00d7 85 = 543,660\n3473 \u00d7 29 = 100,717\n543,660 \u2212 100,717 + 157 = 443,100\n\nAnswer: 443100"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-195642", "out": "1435 \u00d7 14 = 20090\n2927 \u00d7 74 = 216598\n20090 \u2212 216598 + 866 = \u2212195642\n\nAnswer: -195642"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-445003", "out": "1524 \u00d7 32 = 48,768\n5260 \u00d7 94 = 494,440\n48,768 \u2212 494,440 + 669 = \u2212445,003\n\nAnswer: -445003"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "176326", "out": "7242 \u00d7 67 = 485,214\n9660 \u00d7 32 = 309,120\n485,214 \u2212 309,120 = 176,094\n176,094 + 232 = 176,326\n\nAnswer: 176326"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-276361", "out": "2754 \u00d7 24 = 66,096\n5539 \u00d7 62 = 343,418\n66,096 \u2212 343,418 + 961 = \u2212276,361\n\nAnswer: -276361"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "137387", "out": "7277 \u00d7 24 = 174,648\n1783 \u00d7 21 = 37,443\n174,648 \u2212 37,443 = 137,205\n137,205 + 182 = 137,387\n\nAnswer: 137387"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-713347", "out": "2223 \u00d7 48 = 106,704\n9546 \u00d7 86 = 820,956\n106,704 \u2212 820,956 = \u2212714,252\n\u2212714,252 + 905 = \u2212713,347\n\nAnswer: -713347"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-184807", "out": "6223 \u00d7 51 = 317,373\n9479 \u00d7 53 = 502,387\n317,373 \u2212 502,387 = \u2212185,014\n\u2212185,014 + 207 = \u2212184,807\n\nAnswer: -184807"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-110976", "out": "7602 \u00d7 38 = 288,876\n6911 \u00d7 58 = 400,838\n288,876 \u2212 400,838 = \u2212111,962\n\u2212111,962 + 986 = \u2212110,976\n\nAnswer: -110976"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "41509", "out": "9701 \u00d7 42 = 407,442\n9171 \u00d7 40 = 366,840\n407,442 \u2212 366,840 = 40,602\n40,602 + 907 = 41,509\n\nAnswer: 41509"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-380080", "out": "5725 \u00d7 30 = 171,750\n9522 \u00d7 58 = 552,276\n171,750 \u2212 552,276 + 446 = \u2212380,080\n\nAnswer: -380080"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "228699", "out": "7167 \u00d7 93 = 666,531\n5477 \u00d7 80 = 438,160\n666,531 \u2212 438,160 = 228,371\n228,371 + 328 = 228,699\n\nAnswer: 228699"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-28651", "out": "8941 \u00d7 78 = 697,398\n8549 \u00d7 85 = 726,665\n697,398 \u2212 726,665 = \u221229,267\n\u221229,267 + 616 = \u221228,651\n\nAnswer: -28651"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "165921", "out": "3720 \u00d7 70 = 260,400\n1161 \u00d7 82 = 95,202\n260,400 \u2212 95,202 = 165,198\n165,198 + 723 = 165,921\n\nAnswer: 165921"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-278039", "out": "5353 \u00d7 63 = 337,239\n6413 \u00d7 96 = 615,648\n337,239 \u2212 615,648 = \u2212278,409\n\u2212278,409 + 370 = \u2212278,039\n\nAnswer: -278039"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-108535", "out": "2034 \u00d7 69 = 140,346\n5540 \u00d7 45 = 249,300\n140,346 \u2212 249,300 = \u2212108,954\n\u2212108,954 + 419 = \u2212108,535\n\nAnswer: -108535"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-561130", "out": "4801 \u00d7 13 = 62,413\n6499 \u00d7 96 = 623,904\n62,413 \u2212 623,904 + 361 = \u2212561,130\n\nAnswer: -561130"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-329059", "out": "2687 \u00d7 35 = 94,045\n4988 \u00d7 85 = 423,980\n94,045 \u2212 423,980 + 876 = \u2212329,059\n\nAnswer: -329059"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-584058", "out": "1669 \u00d7 60 = 100,140\n7059 \u00d7 97 = 684,723\n100,140 \u2212 684,723 + 525 = \u2212584,058\n\nAnswer: -584058"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-90863", "out": "2598 \u00d7 31 = 80,538\n3514 \u00d7 49 = 172,186\n80,538 \u2212 172,186 = \u221291,648\n\u221291,648 + 785 = \u221290,863\n\nAnswer: -90863"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-49815", "out": "6525 \u00d7 28 = 182,700\n9308 \u00d7 25 = 232,700\n182,700 \u2212 232,700 = \u221250,000\n\u221250,000 + 185 = \u221249,815\n\nAnswer: -49815"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-170174", "out": "2206 \u00d7 55 = 121,330\n7691 \u00d7 38 = 292,258\n121,330 \u2212 292,258 = \u2212170,928\n\u2212170,928 + 754 = \u2212170,174\n\nAnswer: -170174"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-131498", "out": "5088 \u00d7 56 = 284,928\n8685 \u00d7 48 = 416,880\n284,928 \u2212 416,880 = \u2212131,952\n\u2212131,952 + 454 = \u2212131,498\n\nAnswer: -131498"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-238910", "out": "7042 \u00d7 16 = 112,672\n7337 \u00d7 48 = 352,176\n112,672 \u2212 352,176 = \u2212239,504\n\u2212239,504 + 594 = \u2212238,910\n\nAnswer: -238910"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "136325", "out": "4187 \u00d7 64 = 267,968\n2645 \u00d7 50 = 132,250\n267,968 \u2212 132,250 = 135,718\n135,718 + 607 = 136,325\n\nAnswer: 136325"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-353650", "out": "5511 \u00d7 78 = 429,858\n9225 \u00d7 85 = 784,125\n429,858 \u2212 784,125 + 617 = \u2212353,650\n\nAnswer: -353650"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "649051", "out": "9175 \u00d7 87 = 798,225\n6242 \u00d7 24 = 149,808\n798,225 \u2212 149,808 = 648,417\n648,417 + 634 = 649,051\n\nAnswer: 649051"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-206372", "out": "8754 \u00d7 27 = 236,358\n6238 \u00d7 71 = 442,898\n236,358 \u2212 442,898 + 168 = \u2212206,372\n\nAnswer: -206372"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "376484", "out": "9921 \u00d7 47 = 466,287\n6035 \u00d7 15 = 90,525\n466,287 \u2212 90,525 = 375,762\n375,762 + 722 = 376,484\n\nAnswer: 376484"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "190532", "out": "6168 \u00d7 52 = 320,736\n3444 \u00d7 38 = 130,872\n320,736 \u2212 130,872 = 189,864\n189,864 + 668 = 190,532\n\nAnswer: 190532"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "130853", "out": "6819 \u00d7 54 = 368,226\n4863 \u00d7 49 = 238,287\n368,226 \u2212 238,287 = 129,939\n129,939 + 914 = 130,853\n\nAnswer: 130853"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-79730", "out": "8187 \u00d7 38 = 311,106\n6995 \u00d7 56 = 391,720\n311,106 \u2212 391,720 = \u221280,614\n\u221280,614 + 884 = \u221279,730\n\nAnswer: -79730"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "372480", "out": "5768 \u00d7 88 = 507,584\n9048 \u00d7 15 = 135,720\n507,584 \u2212 135,720 = 371,864\n371,864 + 616 = 372,480\n\nAnswer: 372480"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-224669", "out": "2633 \u00d7 20 = 52,660\n4880 \u00d7 57 = 278,160\n52,660 \u2212 278,160 + 831 = \u2212224,669\n\nAnswer: -224669"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "171986", "out": "9247 \u00d7 24 = 221,928\n1620 \u00d7 31 = 50,220\n221,928 \u2212 50,220 = 171,708\n171,708 + 278 = 171,986\n\nAnswer: 171986"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-126408", "out": "1069 \u00d7 60 = 64,140\n2273 \u00d7 84 = 190,932\n64,140 \u2212 190,932 + 384 = \u2212126,408\n\nAnswer: -126408"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-48048", "out": "4237 \u00d7 19 = 80,503\n5609 \u00d7 23 = 129,007\n80,503 \u2212 129,007 = \u221248,504\n\u221248,504 + 456 = \u221248,048\n\nAnswer: -48048"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-18682", "out": "6261 \u00d7 40 = 250,440\n7926 \u00d7 34 = 269,484\n250,440 \u2212 269,484 = \u221219,044\n\u221219,044 + 362 = \u221218,682\n\nAnswer: -18682"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "93433", "out": "3049 \u00d7 83 = 253,067\n1823 \u00d7 88 = 160,424\n253,067 \u2212 160,424 = 92,643\n92,643 + 790 = 93,433\n\nAnswer: 93433"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-264534", "out": "6036 \u00d7 24 = 144,864\n5692 \u00d7 72 = 409,824\n144,864 \u2212 409,824 = \u2212264,960\n\u2212264,960 + 426 = \u2212264,534\n\nAnswer: -264534"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "241272", "out": "6097 \u00d7 68 = 414,596\n7227 \u00d7 24 = 173,448\n414,596 \u2212 173,448 = 241,148\n241,148 + 124 = 241,272\n\nAnswer: 241272"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "407077", "out": "8453 \u00d7 88 = 743,864\n3787 \u00d7 89 = 337,043\n743,864 \u2212 337,043 = 406,821\n406,821 + 256 = 407,077\n\nAnswer: 407077"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-63597", "out": "3233 \u00d7 71 = 229,543\n5653 \u00d7 52 = 293,956\n229,543 \u2212 293,956 = \u221264,413\n\u221264,413 + 816 = \u221263,597\n\nAnswer: -63597"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "8579", "out": "2520 \u00d7 96 = 241,920\n9008 \u00d7 26 = 234,208\n241,920 \u2212 234,208 = 7,712\n7,712 + 867 = 8,579\n\nAnswer: 8579"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "72266", "out": "7583 \u00d7 23 = 174,409\n3306 \u00d7 31 = 102,486\n174,409 \u2212 102,486 = 71,923\n71,923 + 343 = 72,266\n\nAnswer: 72266"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-303638", "out": "8824 \u00d7 41 = 361,784\n9377 \u00d7 71 = 665,767\n361,784 \u2212 665,767 = \u2212303,983\n\u2212303,983 + 345 = \u2212303,638\n\nAnswer: -303638"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-112969", "out": "3074 \u00d7 66 = 202,884\n3599 \u00d7 88 = 316,712\n202,884 \u2212 316,712 = \u2212113,828\n\u2212113,828 + 859 = \u2212112,969\n\nAnswer: -112969"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "511530", "out": "6324 \u00d7 88 = 556,512\n1573 \u00d7 29 = 45,617\n556,512 \u2212 45,617 = 510,895\n510,895 + 635 = 511,530\n\nAnswer: 511530"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-417515", "out": "5827 \u00d7 15 = 87,405\n6243 \u00d7 81 = 505,683\n87,405 \u2212 505,683 + 763 = \u2212417,515\n\nAnswer: -417515"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "267298", "out": "6824 \u00d7 59 = 6824 \u00d7 60 \u2212 6824 = 409,440 \u2212 6,824 = 402,616\n9085 \u00d7 15 = 136,275\n402,616 \u2212 136,275 = 266,341\n266,341 + 957 = 267,298\n\nAnswer: 267298"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-542711", "out": "2125 \u00d7 73 = 155,125\n8221 \u00d7 85 = 698,785\n155,125 \u2212 698,785 = \u2212543,660\n\u2212543,660 + 949 = \u2212542,711\n\nAnswer: -542711"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-230441", "out": "7743 \u00d7 37 = 286,491\n7724 \u00d7 67 = 517,508\n286,491 \u2212 517,508 + 576 = \u2212230,441\n\nAnswer: -230441"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-150063", "out": "8956 \u00d7 22 = 197,032\n7095 \u00d7 49 = 347,655\n197,032 \u2212 347,655 + 560 = \u2212150,063\n\nAnswer: -150063"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-45751", "out": "6483 \u00d7 92 = 596,436\n7740 \u00d7 83 = 642,420\n596,436 \u2212 642,420 + 233 = \u221245,751\n\nAnswer: -45751"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "349226", "out": "6876 \u00d7 77 = 529,452\n7526 \u00d7 24 = 180,624\n529,452 \u2212 180,624 = 348,828\n348,828 + 398 = 349,226\n\nAnswer: 349226"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "126", "out": "3455 \u00d7 73 = 252,215\n2836 \u00d7 89 = 252,404\n252,215 \u2212 252,404 + 315 = \u2212189 + 315 = 126\n\nAnswer: 126"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "404495", "out": "09 = 693,810 + 7,709 = 701,519\n8507 \u00d7 35 = 8507 \u00d7 30 + 8507 \u00d7 5 = 255,210 + 42,535 = 297,745\n701,519 \u2212 297,745 = 403,774\n403,774 + 721 = 404,495\n\nAnswer: 404495"} +{"tag": "rc4-on", "kind": "arith", "ok": false, "answer": "140586", "out": "8753 \u00d7 92 = 788,770 + 17,506 = 806,276\n8117 \u00d7 82 = 649,360 + 16,234 = 665,594\n806,276 \u2212 665,594 = 140,682\n140,682 + 904 = 141,586\n\nAnswer: 141586"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-29023", "out": "4060 \u00d7 64 = 259,840\n2987 \u00d7 97 = 289,739\n259,840 \u2212 289,739 + 876 = \u221229,023\n\nAnswer: -29023"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "50695", "out": "6695 \u00d7 82 = 548,990\n9774 \u00d7 51 = 498,474\n548,990 \u2212 498,474 = 50,516\n50,516 + 179 = 50,695\n\nAnswer: 50695"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-16815", "out": "7609 \u00d7 25 = 190,225\n3289 \u00d7 63 = 207,207\n190,225 \u2212 207,207 + 167 = \u221216,815\n\nAnswer: -16815"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "826865", "out": "97 \u00d7 97 = 9197 \u00d7 100 \u2212 9197 \u00d7 3 = 919700 \u2212 27591 = 892109\n2452 \u00d7 27 = 2452 \u00d7 20 + 2452 \u00d7 7 = 49040 + 17164 = 66204\n892109 \u2212 66204 + 960 = 826865\n\nAnswer: 826865"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "155143", "out": "6494 \u00d7 44 = 285,736\n2118 \u00d7 62 = 131,316\n285,736 \u2212 131,316 = 154,420\n154,420 + 723 = 155,143\n\nAnswer: 155143"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "114189", "out": "3891 \u00d7 91 = 354,081\n3762 \u00d7 64 = 240,768\n354,081 \u2212 240,768 = 113,313\n113,313 + 876 = 114,189\n\nAnswer: 114189"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "370597", "out": "7173 \u00d7 89 = 638,397\n8669 \u00d7 31 = 268,739\n638,397 \u2212 268,739 = 369,658\n369,658 + 939 = 370,597\n\nAnswer: 370597"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-46144", "out": "8653 \u00d7 14 = 121,142\n5406 \u00d7 31 = 167,586\n121,142 \u2212 167,586 + 300 = \u221246,144\n\nAnswer: -46144"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "174618", "out": "6654 \u00d7 73 = 485,742\n6501 \u00d7 48 = 312,048\n485,742 \u2212 312,048 = 173,694\n173,694 + 924 = 174,618\n\nAnswer: 174618"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-71328", "out": "5549 \u00d7 29 = 160,921\n8626 \u00d7 27 = 232,902\n160,921 \u2212 232,902 = \u221271,981\n\u221271,981 + 653 = \u221271,328\n\nAnswer: -71328"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-85671", "out": "2253 \u00d7 91 = 205,023\n5197 \u00d7 56 = 291,032\n205,023 \u2212 291,032 + 338 = \u221286,009 + 338 = \u221285,671\n\nAnswer: -85671"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "78091", "out": "\u2212 6496 = 519,680 \u2212 6,496 = 513,184\n6048 \u00d7 72 = 6048 \u00d7 70 + 6048 \u00d7 2 = 423,360 + 12,096 = 435,456\n513,184 \u2212 435,456 = 77,728\n77,728 + 363 = 78,091\n\nAnswer: 78091"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-86911", "out": "3599 \u00d7 45 = 161,955\n9994 \u00d7 25 = 249,850\n161,955 \u2212 249,850 = \u221287,895\n\u221287,895 + 984 = \u221286,911\n\nAnswer: -86911"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "205431", "out": "5222 \u00d7 66 = 344,652\n2335 \u00d7 60 = 140,100\n344,652 \u2212 140,100 = 204,552\n204,552 + 879 = 205,431\n\nAnswer: 205431"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "212206", "out": "6604 \u00d7 75 = 495,300\n4291 \u00d7 66 = 283,206\n495,300 \u2212 283,206 = 212,094\n212,094 + 112 = 212,206\n\nAnswer: 212206"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "383600", "out": "8998 \u00d7 56 = 503,888\n1861 \u00d7 65 = 120,965\n503,888 \u2212 120,965 = 382,923\n382,923 + 677 = 383,600\n\nAnswer: 383600"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "29108", "out": "2906 \u00d7 53 = 154,018\n4055 \u00d7 31 = 125,705\n154,018 \u2212 125,705 = 28,313\n28,313 + 795 = 29,108\n\nAnswer: 29108"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-388311", "out": "2895 \u00d7 19 = 55005\n5346 \u00d7 83 = 443718\n55005 \u2212 443718 + 402 = \u2212388311\n\nAnswer: -388311"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-278321", "out": "5991 \u00d7 74 = 443,334\n8303 \u00d7 87 = 722,361\n443,334 \u2212 722,361 = \u2212279,027\n\u2212279,027 + 706 = \u2212278,321\n\nAnswer: -278321"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "156958", "out": "3955 \u00d7 57 = 225,435\n1324 \u00d7 52 = 68,848\n225,435 \u2212 68,848 = 156,587\n156,587 + 371 = 156,958\n\nAnswer: 156958"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-209685", "out": "5067 \u00d7 36 = 182,412\n4177 \u00d7 94 = 392,638\n182,412 \u2212 392,638 = \u2212210,226\n\u2212210,226 + 541 = \u2212209,685\n\nAnswer: -209685"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-60104", "out": "4168 \u00d7 96 = 400,128\n6058 \u00d7 76 = 460,408\n400,128 \u2212 460,408 + 176 = \u221260,104\n\nAnswer: -60104"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-12633", "out": "7021 \u00d7 34 = 238,714\n3072 \u00d7 82 = 251,904\n238,714 \u2212 251,904 = \u221213,190\n\u221213,190 + 557 = \u221212,633\n\nAnswer: -12633"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "254804", "out": "3716 \u00d7 90 = 334,440\n1462 \u00d7 55 = 80,410\n334,440 \u2212 80,410 = 254,030\n254,030 + 774 = 254,804\n\nAnswer: 254804"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-54951", "out": "3963 \u00d7 35 = 138,705\n4405 \u00d7 44 = 193,820\n138,705 \u2212 193,820 = \u221255,115\n\u221255,115 + 164 = \u221254,951\n\nAnswer: -54951"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-18116", "out": "5232 \u00d7 26 = 136,032\n2066 \u00d7 75 = 154,950\n136,032 \u2212 154,950 + 802 = \u221218,116\n\nAnswer: -18116"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "70171", "out": "2966 \u00d7 51 = 151,266\n1540 \u00d7 53 = 81,620\n151,266 \u2212 81,620 = 69,646\n69,646 + 525 = 70,171\n\nAnswer: 70171"} +{"tag": "rc4-on", "kind": "arith", "ok": false, "answer": "101064", "out": "9812 \u00d7 53 = 520,236\n9332 \u00d7 45 = 419,940\n520,236 \u2212 419,940 = 100,296\n100,296 + 968 = 101,264\n\nAnswer: 101264"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-82095", "out": "7495 \u00d7 69 = 517,155\n9372 \u00d7 64 = 599,808\n517,155 \u2212 599,808 + 558 = \u221282,095\n\nAnswer: -82095"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-61936", "out": "2450 \u00d7 30 = 73,500\n5901 \u00d7 23 = 135,723\n73,500 \u2212 135,723 + 287 = \u221262,223 + 287 = \u221261,936\n\nAnswer: -61936"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-96402", "out": "8325 \u00d7 83 = 690,975\n8949 \u00d7 88 = 787,512\n690,975 \u2212 787,512 = \u221296,537\n\u221296,537 + 135 = \u221296,402\n\nAnswer: -96402"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "81765", "out": "4878 \u00d7 51 = 248,778\n5066 \u00d7 33 = 167,178\n248,778 \u2212 167,178 = 81,600\n81,600 + 165 = 81,765\n\nAnswer: 81765"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "55365", "out": "4710 \u00d7 60 = 282,600\n8151 \u00d7 28 = 228,228\n282,600 \u2212 228,228 = 54,372\n54,372 + 993 = 55,365\n\nAnswer: 55365"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "12719", "out": "7030 \u00d7 26 = 182,780\n2748 \u00d7 62 = 170,376\n182,780 \u2212 170,376 = 12,404\n12,404 + 315 = 12,719\n\nAnswer: 12719"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-113286", "out": "5695 \u00d7 57 = 324,615\n7832 \u00d7 56 = 438,592\n324,615 \u2212 438,592 = \u2212113,977\n\u2212113,977 + 691 = \u2212113,286\n\nAnswer: -113286"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-294892", "out": "7567 \u00d7 43 = 325,381\n9131 \u00d7 68 = 620,908\n325,381 \u2212 620,908 = \u2212295,527\n\u2212295,527 + 635 = \u2212294,892\n\nAnswer: -294892"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-89555", "out": "2454 \u00d7 70 = 171,780\n2725 \u00d7 96 = 261,600\n171,780 \u2212 261,600 = \u221289,820\n\u221289,820 + 265 = \u221289,555\n\nAnswer: -89555"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "396623", "out": "8603 \u00d7 79 = 679,637\n3837 \u00d7 74 = 283,938\n679,637 \u2212 283,938 = 395,699\n395,699 + 924 = 396,623\n\nAnswer: 396623"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "43550", "out": "9400 \u00d7 40 = 376,000\n7395 \u00d7 45 = 332,775\n376,000 \u2212 332,775 = 43,225\n43,225 + 325 = 43,550\n\nAnswer: 43550"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-540379", "out": "5715 \u00d7 36 = 205,740\n8685 \u00d7 86 = 746,910\n205,740 \u2212 746,910 = \u2212541,170\n\u2212541,170 + 791 = \u2212540,379\n\nAnswer: -540379"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-122358", "out": "2314 \u00d7 84 = 194,376\n6742 \u00d7 47 = 316,874\n194,376 \u2212 316,874 + 140 = \u2212122,358\n\nAnswer: -122358"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "409753", "out": "5260 \u00d7 89 = 468,140\n1834 \u00d7 32 = 58,688\n468,140 \u2212 58,688 = 409,452\n409,452 + 301 = 409,753\n\nAnswer: 409753"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-330799", "out": "6878 \u00d7 54 = 371,412\n8370 \u00d7 84 = 703,080\n371,412 \u2212 703,080 = \u2212331,668\n\u2212331,668 + 869 = \u2212330,799\n\nAnswer: -330799"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-237705", "out": "1781 \u00d7 90 = 160,290\n8658 \u00d7 46 = 398,268\n160,290 \u2212 398,268 + 273 = \u2212237,705\n\nAnswer: -237705"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-153904", "out": "9018 \u00d7 29 = 261,522\n5779 \u00d7 72 = 416,088\n261,522 \u2212 416,088 = \u2212154,566\n\u2212154,566 + 662 = \u2212153,904\n\nAnswer: -153904"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "265113", "out": "4398 \u00d7 95 = 4398 \u00d7 100 \u2212 4398 \u00d7 5 = 439800 \u2212 21990 = 417810\n1705 \u00d7 90 = 153450\n417810 \u2212 153450 = 264360\n264360 + 753 = 265113\n\nAnswer: 265113"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-47438", "out": "1947 \u00d7 26 = 50,622\n1371 \u00d7 72 = 98,712\n50,622 \u2212 98,712 + 652 = \u221247,438\n\nAnswer: -47438"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "30899", "out": "5973 \u00d7 25 = 149,325\n2197 \u00d7 54 = 118,638\n149,325 \u2212 118,638 = 30,687\n30,687 + 212 = 30,899\n\nAnswer: 30899"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "414096", "out": "5880 \u00d7 95 = 558,600\n6291 \u00d7 23 = 144,693\n558,600 \u2212 144,693 = 413,907\n413,907 + 189 = 414,096\n\nAnswer: 414096"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-377848", "out": "2050 \u00d7 42 = 86,100\n6369 \u00d7 73 = 464,937\n86,100 \u2212 464,937 + 989 = \u2212377,848\n\nAnswer: -377848"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-12260", "out": "2233 \u00d7 16 = 35,728\n2117 \u00d7 23 = 48,691\n35,728 \u2212 48,691 = \u221212,963\n\u221212,963 + 703 = \u221212,260\n\nAnswer: -12260"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-276957", "out": "3721 \u00d7 90 = 334,890\n6313 \u00d7 97 = 631,300 \u2212 18,939 = 612,361\n334,890 \u2212 612,361 + 514 = \u2212276,957\n\nAnswer: -276957"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-268149", "out": "1163 \u00d7 86 = 100,018\n9000 \u00d7 41 = 369,000\n100,018 \u2212 369,000 + 833 = \u2212268,149\n\nAnswer: -268149"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "589317", "out": "7627 \u00d7 86 = 655,922\n3931 \u00d7 17 = 66,827\n655,922 \u2212 66,827 + 222 = 589,095 + 222 = 589,317\n\nAnswer: 589317"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-122292", "out": "1189 \u00d7 34 = 40,426\n2768 \u00d7 59 = 163,312\n40,426 \u2212 163,312 + 594 = \u2212122,292\n\nAnswer: -122292"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-299538", "out": "2909 \u00d7 66 = 191,994\n9459 \u00d7 52 = 491,868\n191,994 \u2212 491,868 + 336 = \u2212299,538\n\nAnswer: -299538"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "276153", "out": "7019 \u00d7 69 = 484,311\n8341 \u00d7 25 = 208,525\n484,311 \u2212 208,525 = 275,786\n275,786 + 367 = 276,153\n\nAnswer: 276153"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-399291", "out": "Factor out 88: (2379 \u2212 6926) \u00d7 88 + 845\n= (\u22124547) \u00d7 88 + 845\n= \u2212400136 + 845\n= \u2212399291\n\nAnswer: -399291"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-66758", "out": "1247 \u00d7 59 = 73,573\n2995 \u00d7 47 = 140,765\n73,573 \u2212 140,765 + 434 = \u221266,758\n\nAnswer: -66758"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-380329", "out": "1288 \u00d7 96 = 123,648\n5800 \u00d7 87 = 504,600\n123,648 \u2212 504,600 + 623 = \u2212380,329\n\nAnswer: -380329"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-198813", "out": "1088 \u00d7 56 = 60,928\n2801 \u00d7 93 = 260,493\n60,928 \u2212 260,493 + 752 = \u2212198,813\n\nAnswer: -198813"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-7903", "out": "3203 \u00d7 41 = 131,323\n3246 \u00d7 43 = 139,578\n131,323 \u2212 139,578 = \u22128,255\n\u22128,255 + 352 = \u22127,903\n\nAnswer: -7903"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "338776", "out": "9267 \u00d7 84 = 778,428\n6872 \u00d7 64 = 439,808\n778,428 \u2212 439,808 = 338,620\n338,620 + 156 = 338,776\n\nAnswer: 338776"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "227531", "out": "8316 \u00d7 79 = 656,964\n9357 \u00d7 46 = 430,422\n656,964 \u2212 430,422 = 226,542\n226,542 + 989 = 227,531\n\nAnswer: 227531"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-244230", "out": "1380 \u00d7 82 = 113,160\n5969 \u00d7 60 = 358,140\n113,160 \u2212 358,140 + 750 = \u2212244,230\n\nAnswer: -244230"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-301848", "out": "3242 \u00d7 17 = 55,114\n6501 \u00d7 55 = 357,555\n55,114 \u2212 357,555 = \u2212302,441\n\u2212302,441 + 593 = \u2212301,848\n\nAnswer: -301848"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-224010", "out": "6377 \u00d7 61 = 388,997\n6388 \u00d7 96 = 613,248\n388,997 \u2212 613,248 + 241 = \u2212224,010\n\nAnswer: -224010"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-30955", "out": "1603 \u00d7 13 = 20839\n3291 \u00d7 16 = 52656\n20839 \u2212 52656 + 862 = \u221231817 + 862 = \u221230955\n\nAnswer: -30955"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "82011", "out": "9528 \u00d7 27 = 257,256\n8780 \u00d7 20 = 175,600\n257,256 \u2212 175,600 = 81,656\n81,656 + 355 = 82,011\n\nAnswer: 82011"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "32409", "out": "3854 \u00d7 58 = 223,532\n2253 \u00d7 85 = 191,505\n223,532 \u2212 191,505 = 32,027\n32,027 + 382 = 32,409\n\nAnswer: 32409"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "49681", "out": "4290 \u00d7 42 = 180,180\n6563 \u00d7 20 = 131,260\n180,180 \u2212 131,260 = 48,920\n48,920 + 761 = 49,681\n\nAnswer: 49681"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "55954", "out": "4850 \u00d7 41 = 198,850\n1559 \u00d7 92 = 143,428\n198,850 \u2212 143,428 = 55,422\n55,422 + 532 = 55,954\n\nAnswer: 55954"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-765927", "out": "5753 \u00d7 21 = 120,813\n9244 \u00d7 96 = 887,424\n120,813 \u2212 887,424 + 684 = \u2212765,927\n\nAnswer: -765927"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-17105", "out": "8007 \u00d7 62 = 496,434\n9512 \u00d7 54 = 513,648\n496,434 \u2212 513,648 = \u221217,214\n\u221217,214 + 109 = \u221217,105\n\nAnswer: -17105"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-181974", "out": "2408 \u00d7 81 = 195,048\n9679 \u00d7 39 = 377,481\n195,048 \u2212 377,481 + 459 = \u2212181,974\n\nAnswer: -181974"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-16426", "out": "8046 \u00d7 30 = 241,380\n4613 \u00d7 56 = 258,328\n241,380 \u2212 258,328 + 522 = \u221216,426\n\nAnswer: -16426"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-358261", "out": "1706 \u00d7 25 = 42,650\n9344 \u00d7 43 = 401,792\n42,650 \u2212 401,792 + 881 = \u2212358,261\n\nAnswer: -358261"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-16385", "out": "8938 \u00d7 19 = 169,822\n2278 \u00d7 82 = 186,796\n169,822 \u2212 186,796 = \u221216,974\n\u221216,974 + 589 = \u221216,385\n\nAnswer: -16385"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-104940", "out": "9193 \u00d7 22 = 202,246\n5124 \u00d7 60 = 307,440\n202,246 \u2212 307,440 + 254 = \u2212104,940\n\nAnswer: -104940"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "369460", "out": "9279 \u00d7 57 = 528,903\n3560 \u00d7 45 = 160,200\n528,903 \u2212 160,200 = 368,703\n368,703 + 757 = 369,460\n\nAnswer: 369460"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "100772", "out": "5604 \u00d7 29 = 162,516\n1063 \u00d7 59 = 62,717\n162,516 \u2212 62,717 = 99,799\n99,799 + 973 = 100,772\n\nAnswer: 100772"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "66339", "out": "7070 \u00d7 30 = 212,100\n2989 \u00d7 49 = 2989 \u00d7 50 \u2212 2989 = 149,450 \u2212 2,989 = 146,461\n212,100 \u2212 146,461 + 700 = 66,339\n\nAnswer: 66339"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "472525", "out": "8596 \u00d7 80 = 687,680\n3314 \u00d7 65 = 215,410\n687,680 \u2212 215,410 = 472,270\n472,270 + 255 = 472,525\n\nAnswer: 472525"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "238859", "out": "8035 \u00d7 47 = 8035 \u00d7 40 + 8035 \u00d7 7 = 321400 + 56245 = 377645\n1742 \u00d7 80 = 139360\n377645 \u2212 139360 = 238285\n238285 + 574 = 238859\n\nAnswer: 238859"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "581380", "out": "0 \u2212 7499 \u00d7 3 = 749900 \u2212 22497 = 727403\n5219 \u00d7 28 = 5219 \u00d7 30 \u2212 5219 \u00d7 2 = 156570 \u2212 10438 = 146132\n727403 \u2212 146132 = 581271\n581271 + 109 = 581380\n\nAnswer: 581380"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-216360", "out": "1385 \u00d7 30 = 41,550\n6985 \u00d7 37 = 258,445\n41,550 \u2212 258,445 + 535 = \u2212216,360\n\nAnswer: -216360"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "63901", "out": "4823 \u00d7 96 = 463,008\n8693 \u00d7 46 = 399,878\n463,008 \u2212 399,878 = 63,130\n63,130 + 771 = 63,901\n\nAnswer: 63901"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "89176", "out": "8180 \u00d7 28 = 229,040\n1523 \u00d7 92 = 140,116\n229,040 \u2212 140,116 = 88,924\n88,924 + 252 = 89,176\n\nAnswer: 89176"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "52179", "out": "3692 \u00d7 80 = 295,360\n3989 \u00d7 61 = 243,329\n295,360 \u2212 243,329 = 52,031\n52,031 + 148 = 52,179\n\nAnswer: 52179"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-13419", "out": "4831 \u00d7 77 = 338170 + 33817 = 371987\n4195 \u00d7 92 = 377550 + 8390 = 385940\n371987 \u2212 385940 + 534 = \u221213953 + 534 = \u221213419\n\nAnswer: -13419"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-311919", "out": "3113 \u00d7 89 = 277,057\n9207 \u00d7 64 = 589,248\n277,057 \u2212 589,248 + 272 = \u2212311,919\n\nAnswer: -311919"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "46589", "out": "2006 \u00d7 73 = 146,438\n1154 \u00d7 87 = 100,398\n146,438 \u2212 100,398 = 46,040\n46,040 + 549 = 46,589\n\nAnswer: 46589"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-334650", "out": "8102 \u00d7 16 = 129,632\n6646 \u00d7 70 = 465,220\n129,632 \u2212 465,220 = \u2212335,588\n\u2212335,588 + 938 = \u2212334,650\n\nAnswer: -334650"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-27964", "out": "2657 \u00d7 92 = 244,444\n3365 \u00d7 81 = 272,565\n244,444 \u2212 272,565 = \u221228,121\n\u221228,121 + 157 = \u221227,964\n\nAnswer: -27964"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "322893", "out": "8781 \u00d7 54 = 474,174\n5076 \u00d7 30 = 152,280\n474,174 \u2212 152,280 = 321,894\n321,894 + 999 = 322,893\n\nAnswer: 322893"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-248724", "out": "3361 \u00d7 65 = 218,465\n5572 \u00d7 84 = 468,048\n218,465 \u2212 468,048 + 859 = \u2212248,724\n\nAnswer: -248724"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-120749", "out": "3605 \u00d7 87 = 313,635\n7376 \u00d7 59 = 435,184\n313,635 \u2212 435,184 = \u2212121,549\n\u2212121,549 + 800 = \u2212120,749\n\nAnswer: -120749"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-316318", "out": "1959 \u00d7 38 = 74,442\n6409 \u00d7 61 = 390,949\n74,442 \u2212 390,949 + 189 = \u2212316,318\n\nAnswer: -316318"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-661784", "out": "4927 \u00d7 24 = 118,248\n9758 \u00d7 80 = 780,640\n118,248 \u2212 780,640 + 608 = \u2212661,784\n\nAnswer: -661784"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "39047", "out": "8173 \u00d7 17 = 138,941\n3348 \u00d7 30 = 100,440\n138,941 \u2212 100,440 = 38,501\n38,501 + 546 = 39,047\n\nAnswer: 39047"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "50919", "out": "5487 \u00d7 27 = 148,149\n5124 \u00d7 19 = 97,356\n148,149 \u2212 97,356 = 50,793\n50,793 + 126 = 50,919\n\nAnswer: 50919"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "298066", "out": "8382 \u00d7 51 = 427,482\n2279 \u00d7 57 = 129,903\n427,482 \u2212 129,903 = 297,579\n297,579 + 487 = 298,066\n\nAnswer: 298066"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-48179", "out": "9749 \u00d7 28 = 272,972\n9456 \u00d7 34 = 321,504\n272,972 \u2212 321,504 = \u221248,532\n\u221248,532 + 353 = \u221248,179\n\nAnswer: -48179"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-101497", "out": "2344 \u00d7 58 = 135,952\n5537 \u00d7 43 = 238,091\n135,952 \u2212 238,091 + 642 = \u2212101,497\n\nAnswer: -101497"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-268554", "out": "8601 \u00d7 26 = 223,626\n5734 \u00d7 86 = 493,124\n223,626 \u2212 493,124 = \u2212269,498\n\u2212269,498 + 944 = \u2212268,554\n\nAnswer: -268554"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-309509", "out": "5085 \u00d7 77 = 391,545\n8162 \u00d7 86 = 701,932\n391,545 \u2212 701,932 + 878 = \u2212309,509\n\nAnswer: -309509"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-69427", "out": "5560 \u00d7 15 = 83,400\n2642 \u00d7 58 = 153,236\n83,400 \u2212 153,236 = \u221269,836\n\u221269,836 + 409 = \u221269,427\n\nAnswer: -69427"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "198975", "out": "3017 \u00d7 76 = 229,292\n2222 \u00d7 14 = 31,108\n229,292 \u2212 31,108 = 198,184\n198,184 + 791 = 198,975\n\nAnswer: 198975"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-181624", "out": "1428 \u00d7 74 = 105,672\n5648 \u00d7 51 = 288,048\n105,672 \u2212 288,048 + 752 = \u2212181,624\n\nAnswer: -181624"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "3339", "out": "4523 \u00d7 25 = 113,075\n3935 \u00d7 28 = 110,180\n113,075 \u2212 110,180 = 2,895\n2,895 + 444 = 3,339\n\nAnswer: 3339"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-253162", "out": "9348 \u00d7 26 = 243,048\n9203 \u00d7 54 = 496,962\n243,048 \u2212 496,962 + 752 = \u2212253,162\n\nAnswer: -253162"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "86525", "out": "2827 \u00d7 36 = 101,772\n1005 \u00d7 16 = 16,080\n101,772 \u2212 16,080 = 85,692\n85,692 + 833 = 86,525\n\nAnswer: 86525"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "651584", "out": "7958 \u00d7 88 = 700,304\n1772 \u00d7 28 = 49,616\n700,304 \u2212 49,616 = 650,688\n650,688 + 896 = 651,584\n\nAnswer: 651584"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "86371", "out": "2820 \u00d7 90 = 253,800\n4000 \u00d7 42 = 168,000\n253,800 \u2212 168,000 = 85,800\n85,800 + 571 = 86,371\n\nAnswer: 86371"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-148158", "out": "1057 \u00d7 32 = 33,824\n3036 \u00d7 60 = 182,160\n33,824 \u2212 182,160 + 178 = \u2212148,158\n\nAnswer: -148158"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "40605", "out": "1035 \u00d7 95 = 98,325\n1289 \u00d7 45 = 58,005\n98,325 \u2212 58,005 = 40,320\n40,320 + 285 = 40,605\n\nAnswer: 40605"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "8166", "out": "7397 \u00d7 16 = 118,352\n2525 \u00d7 44 = 111,100\n118,352 \u2212 111,100 = 7,252\n7,252 + 914 = 8,166\n\nAnswer: 8166"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "278229", "out": "8135 \u00d7 60 = 488100\n4572 \u00d7 46 = 210312\n488100 \u2212 210312 + 441 = 278229\n\nAnswer: 278229"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "486330", "out": " \u00d7 100 \u2212 8846 \u00d7 7 = 884600 \u2212 61922 = 822678\n8643 \u00d7 39 = 8643 \u00d7 40 \u2212 8643 = 345720 \u2212 8643 = 337077\n822678 \u2212 337077 = 485601\n485601 + 729 = 486330\n\nAnswer: 486330"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "174750", "out": "4163 \u00d7 54 = 224,802\n2394 \u00d7 21 = 50,274\n224,802 \u2212 50,274 = 174,528\n174,528 + 222 = 174,750\n\nAnswer: 174750"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-129490", "out": "4552 \u00d7 74 = 336,848\n4970 \u00d7 94 = 467,180\n336,848 \u2212 467,180 = \u2212130,332\n\u2212130,332 + 842 = \u2212129,490\n\nAnswer: -129490"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "191220", "out": "9271 \u00d7 50 = 463,550\n2846 \u00d7 96 = 2846 \u00d7 100 \u2212 2846 \u00d7 4 = 284,600 \u2212 11,384 = 273,216\n463,550 \u2212 273,216 = 190,334\n190,334 + 886 = 191,220\n\nAnswer: 191220"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "184119", "out": "3996 \u00d7 70 = 279,720\n1022 \u00d7 94 = 96,068\n279,720 \u2212 96,068 = 183,652\n183,652 + 467 = 184,119\n\nAnswer: 184119"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "174169", "out": "3788 \u00d7 79 = 3788 \u00d7 80 \u2212 3788 = 303040 \u2212 3788 = 299252\n6269 \u00d7 20 = 125380\n299252 \u2212 125380 + 297 = 173872 + 297 = 174169\n\nAnswer: 174169"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "464086", "out": "7169 \u00d7 86 = 616,534\n2948 \u00d7 52 = 153,296\n616,534 \u2212 153,296 = 463,238\n463,238 + 848 = 464,086\n\nAnswer: 464086"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "194847", "out": "2378 \u00d7 95 = 2378 \u00d7 100 \u2212 2378 \u00d7 5 = 237800 \u2212 11890 = 225910\n2429 \u00d7 13 = 31577\n225910 \u2212 31577 + 514 = 194333 + 514 = 194847\n\nAnswer: 194847"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "313197", "out": "6412 \u00d7 61 = 6412 \u00d7 60 + 6412 = 384720 + 6412 = 391132\n3911 \u00d7 20 = 78220\n391132 \u2212 78220 = 312912\n312912 + 285 = 313197\n\nAnswer: 313197"} +{"tag": "rc4-on", "kind": "arith", "ok": false, "answer": "110784", "out": "5245 \u00d7 60 = 314,700\n2497 \u00d7 82 = 204,734\n314,700 \u2212 204,734 = 109,966\n109,966 + 838 = 110,804\n\nAnswer: 110804"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-520720", "out": "2892 \u00d7 63 = 182,196\n7324 \u00d7 96 = 703,104\n182,196 \u2212 703,104 + 188 = \u2212520,720\n\nAnswer: -520720"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "891632", "out": "9872 \u00d7 95 = 9872 \u00d7 100 \u2212 9872 \u00d7 5 = 987200 \u2212 49360 = 937840\n2129 \u00d7 22 = 46838\n937840 \u2212 46838 + 630 = 891002 + 630 = 891632\n\nAnswer: 891632"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "162847", "out": "2559 \u00d7 89 = 2559 \u00d7 90 \u2212 2559 = 230310 \u2212 2559 = 227751\n4098 \u00d7 16 = 65568\n227751 \u2212 65568 = 162183\n162183 + 664 = 162847\n\nAnswer: 162847"} +{"tag": "rc4-on", "kind": "arith", "ok": false, "answer": "-217219", "out": "3943 \u00d7 58 = 228,704\n6039 \u00d7 74 = 446,886\n228,704 \u2212 446,886 + 973 = \u2212217,209\n\nAnswer: -217209"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "79408", "out": "6084 \u00d7 23 = 139,932\n4687 \u00d7 13 = 60,931\n139,932 \u2212 60,931 = 79,001\n79,001 + 407 = 79,408\n\nAnswer: 79408"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-99346", "out": "1929 \u00d7 71 = 136,959\n6235 \u00d7 38 = 236,930\n136,959 \u2212 236,930 = \u221299,971\n\u221299,971 + 625 = \u221299,346\n\nAnswer: -99346"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "279863", "out": "5739 \u00d7 57 = 327,123\n1031 \u00d7 46 = 47,426\n327,123 \u2212 47,426 = 279,697\n279,697 + 166 = 279,863\n\nAnswer: 279863"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "79572", "out": "1994 \u00d7 54 = 107,676\n1782 \u00d7 16 = 28,512\n107,676 \u2212 28,512 = 79,164\n79,164 + 408 = 79,572\n\nAnswer: 79572"} +{"tag": "rc4-on", "kind": "arith", "ok": true, "answer": "-21094", "out": "4973 \u00d7 70 = 348,110\n4510 \u00d7 82 = 369,820\n348,110 \u2212 369,820 + 616 = \u221221,710 + 616 = \u221221,094\n\nAnswer: -21094"} +{"tag": "rc4-on", "kind": "needle", "ok": true, "answer": "24547521", "out": "24547521"} +{"tag": "rc4-on", "kind": "needle", "ok": true, "answer": "35839260", "out": "35839260"} +{"tag": "rc4-on", "kind": "needle", "ok": true, "answer": "43498609", "out": "43498609"} +{"tag": "rc4-on", "kind": "needle", "ok": true, "answer": "31899084", "out": "31899084"} +{"tag": "rc4-on", "kind": "needle", "ok": true, "answer": "90731237", "out": "90731237"} +{"tag": "rc4-on", "kind": "needle", "ok": true, "answer": "08946387", "out": "08946387"} +{"tag": "rc4-on", "kind": "needle", "ok": true, "answer": "72382111", "out": "72382111"} +{"tag": "rc4-on", "kind": "needle", "ok": true, "answer": "04056216", "out": "04056216"} +{"tag": "rc4-on", "kind": "needle", "ok": true, "answer": "00425406", "out": "00425406"} +{"tag": "rc4-on", "kind": "needle", "ok": true, "answer": "84857389", "out": "84857389"} +{"tag": "rc4-on", "kind": "needle", "ok": true, "answer": "33842723", "out": "33842723"} +{"tag": "rc4-on", "kind": "needle", "ok": true, "answer": "67329458", "out": "67329458"} +{"tag": "rc4-on", "kind": "needle", "ok": true, "answer": "25250437", "out": "k942663 -> c352213 -> 25250437\n\n25250437"} +{"tag": "rc4-on", "kind": "needle", "ok": true, "answer": "72172530", "out": "c023756 \u2192 72172530\n\n72172530"} +{"tag": "rc4-on", "kind": "needle", "ok": true, "answer": "90093235", "out": "90093235"} +{"tag": "rc4-on", "kind": "needle", "ok": true, "answer": "43968581", "out": "43968581"} +{"tag": "rc4-on", "kind": "needle", "ok": true, "answer": "14222182", "out": "14222182"} +{"tag": "rc4-on", "kind": "needle", "ok": true, "answer": "39320321", "out": "39320321"} +{"tag": "rc4-on", "kind": "needle", "ok": true, "answer": "79903620", "out": "79903620"} +{"tag": "rc4-on", "kind": "needle", "ok": true, "answer": "04692612", "out": "04692612"} +{"tag": "rc4-on", "kind": "needle", "ok": true, "answer": "44160824", "out": "44160824"} +{"tag": "rc4-on", "kind": "needle", "ok": true, "answer": "75349505", "out": "75349505"} +{"tag": "rc4-on", "kind": "needle", "ok": true, "answer": "62984970", "out": "62984970"} +{"tag": "rc4-on", "kind": "needle", "ok": true, "answer": "49576950", "out": "49576950"} diff --git a/results/2026-10-12-rc4/raw/rc4_on.json b/results/2026-10-12-rc4/raw/rc4_on.json new file mode 100644 index 0000000..6a472e3 --- /dev/null +++ b/results/2026-10-12-rc4/raw/rc4_on.json @@ -0,0 +1,42 @@ +{ + "arm": "rc4_on", + "speed": {}, + "exact": { + "c1": [ + "```python\nRED = True\nBLACK = False\n\n\nclass Node:\n def __init__(self, key, color=RED, left=None, right=None, parent=None):\n self.key = key\n self.color = color\n self.left = left\n self.right = right\n self.parent = parent\n\n\nclass RedBlackTree:\n def __init__(self):\n self.NIL = Node(key=None, color=BLACK)\n self.root = self.NIL\n\n # ---------- rotations ----------\n def _left_rotate(self, x):\n y = x.right\n x.right = y.left\n if y.left != self.NIL:\n y.left.parent = x\n y.parent = x.parent\n if x.parent == self.NIL:\n self.root = y\n elif x == x.parent.left:\n x.parent.left = y\n else:\n x.parent.right = y\n y.left = x\n x.parent = y\n\n def _right_rotate(self, x):\n y = x.left\n x.left = y.right\n if y.right != self.NIL:\n y.right.parent = x\n y.parent = x.parent\n if x.parent ==", + "# The Planets in Order from the Sun\n\n1. **Mercury** \u2014 The smallest planet and closest to the Sun, Mercury has extreme temperature swings between its scorching days and freezing nights.\n\n2. **Venus** \u2014 Wrapped in thick clouds of carbon dioxide, Venus is the hottest planet in the solar system due to a runaway greenhouse effect.\n\n3. **Earth** \u2014 The only known planet to support life, Earth is covered mostly by liquid water and has a single large moon.\n\n4. **Mars** \u2014 The \"Red Planet\" gets its color from iron oxide dust and features the tallest volcano in the solar system, Olympus Mons.\n\n5. **Jupiter** \u2014 The largest planet, Jupiter is a gas giant famous for its Great Red Spot, a storm larger than Earth that has raged for centuries.\n\n6. **Saturn** \u2014 Best known for its stunning ring system made of ice and rock, Saturn is a gas giant so light it could float in water.\n\n7. **Uranus** \u2014 An ice giant that rotates on its side, Uranus has a pale blue-green color caused by methane in its atmosphere.\n\n8. **Neptune** \u2014 The farthest planet from the Sun, Neptune is an ice giant with the fastest", + "# Processes vs. Threads\n\nA **process** is an independent program in execution with its own dedicated memory space, file descriptors, and system resources. When you open a web browser, that's a process; if you also open a text editor, that's a separate process. Processes are heavily isolated from one another\u2014the operating system enforces boundaries so that one process cannot directly access another's memory. This isolation makes processes safer and more stable (a crash in one process doesn't bring down others), but it also makes communication between them more expensive, requiring mechanisms like pipes, sockets, or shared memory segments.\n\nA **thread**, by contrast, is an execution unit *within* a process. Multiple threads can exist inside a single process, and they share that process's memory, open files, and resources. Each thread has its own small, private context\u2014such as a program counter, register values, and stack\u2014but the heap and global variables are common to all threads in the process. This shared state makes threads lightweight: creating a thread is much faster than spawning a process, and switching between threads incurs less overhead. It also makes communication easy, since threads can simply read and write shared data directly.\n\nThe trade-off between the two comes down to isolation versus efficiency.", + "# Library Database Schema\n\n```sql\n-- ============================================\n-- LIBRARY MANAGEMENT SYSTEM SCHEMA\n-- ============================================\n\n-- Books table: stores information about each book title/copy\nCREATE TABLE books (\n book_id INT AUTO_INCREMENT PRIMARY KEY, -- Unique identifier for each book\n title VARCHAR(255) NOT NULL, -- Book title\n author VARCHAR(255) NOT NULL, -- Author name\n isbn VARCHAR(13) UNIQUE, -- International Standard Book Number\n genre VARCHAR(100), -- Genre/category (e.g., Fiction, Science)\n published_year INT, -- Year the book was published\n total_copies INT NOT NULL DEFAULT 1, -- Total copies owned by the library\n available_copies INT NOT NULL DEFAULT 1, -- Copies currently available for loan\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP -- Record creation time\n);\n\n-- Members table: stores library members\nCREATE TABLE members (\n member_id INT AUTO_INCREMENT PRIMARY KEY, -- Unique identifier for each member\n first_name VARCHAR(100) NOT NULL, -- Member's first name\n last_name VARCHAR(100) NOT NULL, -- Member's last name" + ], + "c4a": [ + "```python\nRED = True\nBLACK = False\n\n\nclass Node:\n def __init__(self, key, color=RED, left=None, right=None, parent=None):\n self.key = key\n self.color = color\n self.left = left\n self.right = right\n self.parent = parent\n\n\nclass RedBlackTree:\n def __init__(self):\n self.NIL = Node(key=None, color=BLACK)\n self.root = self.NIL\n\n # ---------- rotations ----------\n def _left_rotate(self, x):\n y = x.right\n x.right = y.left\n if y.left != self.NIL:\n y.left.parent = x\n y.parent = x.parent\n if x.parent == self.NIL:\n self.root = y\n elif x == x.parent.left:\n x.parent.left = y\n else:\n x.parent.right = y\n y.left = x\n x.parent = y\n\n def _right_rotate(self, x):\n y = x.left\n x.left = y.right\n if y.right != self.NIL:\n y.right.parent = x\n y.parent = x.parent\n if x.parent ==", + "# The Planets in Order from the Sun\n\n1. **Mercury** \u2014 The smallest planet and closest to the Sun, Mercury has extreme temperature swings between its scorching days and freezing nights.\n\n2. **Venus** \u2014 Wrapped in thick clouds of carbon dioxide, Venus is the hottest planet in the solar system due to a runaway greenhouse effect.\n\n3. **Earth** \u2014 The only known planet to support life, Earth is covered mostly by liquid water and has a single large moon.\n\n4. **Mars** \u2014 The \"Red Planet\" gets its color from iron oxide dust and features the tallest volcano in the solar system, Olympus Mons.\n\n5. **Jupiter** \u2014 The largest planet, Jupiter is a gas giant famous for its Great Red Spot, a storm larger than Earth that has raged for centuries.\n\n6. **Saturn** \u2014 Best known for its stunning ring system made of ice and rock, Saturn is a gas giant so light it could float in water.\n\n7. **Uranus** \u2014 An ice giant that rotates on its side, Uranus has a pale blue-green color caused by methane in its atmosphere.\n\n8. **Neptune** \u2014 The farthest planet from the Sun, Neptune is an ice giant with the fastest", + "# Processes vs. Threads\n\nA **process** is an independent program in execution with its own dedicated memory space, file descriptors, and system resources. When you open a web browser, that's a process; if you also open a text editor, that's a separate process. Processes are heavily isolated from one another\u2014the operating system enforces boundaries so that one process cannot directly access another's memory. This isolation makes processes safer and more stable (a crash in one process doesn't bring down others), but it also makes communication between them more expensive, requiring mechanisms like pipes, sockets, or shared memory segments.\n\nA **thread**, by contrast, is an execution unit *within* a process. Multiple threads can exist inside a single process, and they share that process's memory, open files, and resources. Each thread has its own small, private context\u2014such as a program counter, register values, and stack\u2014but the heap and global variables are common to all threads in the process. This shared state makes threads lightweight: creating a thread is much faster than spawning a process, and switching between threads incurs less overhead. It also makes communication easy, since threads can simply read and write shared data directly.\n\nThe trade-off between the two comes down to isolation versus efficiency.", + "# Library Database Schema\n\n```sql\n-- ============================================\n-- LIBRARY MANAGEMENT SYSTEM SCHEMA\n-- ============================================\n\n-- Books table: stores information about each book title/copy\nCREATE TABLE books (\n book_id INT AUTO_INCREMENT PRIMARY KEY, -- Unique identifier for each book\n title VARCHAR(255) NOT NULL, -- Book title\n author VARCHAR(255) NOT NULL, -- Author name\n isbn VARCHAR(13) UNIQUE, -- International Standard Book Number\n genre VARCHAR(100), -- Genre/category (e.g., Fiction, Science)\n published_year INT, -- Year the book was published\n total_copies INT NOT NULL DEFAULT 1, -- Total copies owned by the library\n available_copies INT NOT NULL DEFAULT 1, -- Copies currently available for loan\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP -- Record creation time\n);\n\n-- Members table: stores library members\nCREATE TABLE members (\n member_id INT AUTO_INCREMENT PRIMARY KEY, -- Unique identifier for each member\n first_name VARCHAR(100) NOT NULL, -- Member's first name\n last_name VARCHAR(100) NOT NULL, -- Member's last name" + ], + "c4b": [ + "```python\nRED = True\nBLACK = False\n\n\nclass Node:\n def __init__(self, key, color=RED, left=None, right=None, parent=None):\n self.key = key\n self.color = color\n self.left = left\n self.right = right\n self.parent = parent\n\n\nclass RedBlackTree:\n def __init__(self):\n self.NIL = Node(key=None, color=BLACK)\n self.root = self.NIL\n\n # ---------- rotations ----------\n def _left_rotate(self, x):\n y = x.right\n x.right = y.left\n if y.left != self.NIL:\n y.left.parent = x\n y.parent = x.parent\n if x.parent == self.NIL:\n self.root = y\n elif x == x.parent.left:\n x.parent.left = y\n else:\n x.parent.right = y\n y.left = x\n x.parent = y\n\n def _right_rotate(self, x):\n y = x.left\n x.left = y.right\n if y.right != self.NIL:\n y.right.parent = x\n y.parent = x.parent\n if x.parent ==", + "# The Planets in Order from the Sun\n\n1. **Mercury** \u2014 The smallest planet and closest to the Sun, Mercury has extreme temperature swings between its scorching days and freezing nights.\n\n2. **Venus** \u2014 Wrapped in thick clouds of carbon dioxide, Venus is the hottest planet in the solar system due to a runaway greenhouse effect.\n\n3. **Earth** \u2014 The only known planet to support life, Earth is covered mostly by liquid water and has a single large moon.\n\n4. **Mars** \u2014 The \"Red Planet\" gets its color from iron oxide dust and features the tallest volcano in the solar system, Olympus Mons.\n\n5. **Jupiter** \u2014 The largest planet, Jupiter is a gas giant famous for its Great Red Spot, a storm larger than Earth that has raged for centuries.\n\n6. **Saturn** \u2014 Best known for its stunning ring system made of ice and rock, Saturn is a gas giant so light it could float in water.\n\n7. **Uranus** \u2014 An ice giant that rotates on its side, Uranus has a pale blue-green color caused by methane in its atmosphere.\n\n8. **Neptune** \u2014 The farthest planet from the Sun, Neptune is an ice giant with the fastest", + "# Processes vs. Threads\n\nA **process** is an independent program in execution with its own dedicated memory space, file descriptors, and system resources. When you open a web browser, that's a process; if you also open a text editor, that's a separate process. Processes are heavily isolated from one another\u2014the operating system enforces boundaries so that one process cannot directly access another's memory. This isolation makes processes safer and more stable (a crash in one process doesn't bring down others), but it also makes communication between them more expensive, requiring mechanisms like pipes, sockets, or shared memory segments.\n\nA **thread**, by contrast, is an execution unit *within* a process. Multiple threads can exist inside a single process, and they share that process's memory, open files, and resources. Each thread has its own small, private context\u2014such as a program counter, register values, and stack\u2014but the heap and global variables are common to all threads in the process. This shared state makes threads lightweight: creating a thread is much faster than spawning a process, and switching between threads incurs less overhead. It also makes communication easy, since threads can simply read and write shared data directly.\n\nThe trade-off between the two comes down to isolation versus efficiency.", + "# Library Database Schema\n\n```sql\n-- ============================================\n-- LIBRARY MANAGEMENT SYSTEM SCHEMA\n-- ============================================\n\n-- Books table: stores information about each book title/copy\nCREATE TABLE books (\n book_id INT AUTO_INCREMENT PRIMARY KEY, -- Unique identifier for each book\n title VARCHAR(255) NOT NULL, -- Book title\n author VARCHAR(255) NOT NULL, -- Author name\n isbn VARCHAR(13) UNIQUE, -- International Standard Book Number\n genre VARCHAR(100), -- Genre/category (e.g., Fiction, Science)\n published_year INT, -- Year the book was published\n total_copies INT NOT NULL DEFAULT 1, -- Total copies owned by the library\n available_copies INT NOT NULL DEFAULT 1, -- Copies currently available for loan\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP -- Record creation time\n);\n\n-- Members table: stores library members\nCREATE TABLE members (\n member_id INT AUTO_INCREMENT PRIMARY KEY, -- Unique identifier for each member\n first_name VARCHAR(100) NOT NULL, -- Member's first name\n last_name VARCHAR(100) NOT NULL, -- Member's last name" + ], + "c4a_eq_c1": [ + true, + true, + true, + true + ], + "c4b_eq_c1": [ + true, + true, + true, + true + ], + "c4a_eq_c4b": [ + true, + true, + true, + true + ] + } +} \ No newline at end of file diff --git a/results/2026-10-12-rc4/raw/rig-conc.card.txt b/results/2026-10-12-rc4/raw/rig-conc.card.txt new file mode 100644 index 0000000..16fbe0b --- /dev/null +++ b/results/2026-10-12-rc4/raw/rig-conc.card.txt @@ -0,0 +1,28 @@ +╭──────────────────────────────────────────────────────────────────────────────────────────╮ +│ R I G M A R K // AGENT WORKLOAD RECEIPT │ +│ BENCHMARKS LOCAL AI HOW CODING AGENTS ACTUALLY USE IT │ +│ ● 3/3 BASIC OUTPUT GATES PASSED │ +├─ SYSTEM ─────────────────────────────────────────────────────────────────────────────────┤ +│ MODEL glm-5.3-flash-atlas │ +│ APPLIANCE 2 x NVIDIA DGX Spark, GB10, 128 GB unified memory per appliance │ +│ RUN reasoning=low • protocol=1.1.0 │ +│ SOURCE git:40fabcaf6e96 • dirty • worktree:7c17a097cc0d │ +├─ REAL OUTPUT ────────────────────────────────────────────────────────────────────────────┤ +│ WORKLOAD DECODE EST. LAST OUTPUT RANGE BASIC GATE │ +│ CODE 68.5 tok/s 32.4s last 68.5–68.5 ✓ 1/1 │ +│ PROSE 38.4 tok/s 26.8s last 38.4–38.4 ✓ 1/1 │ +│ STRUCTURED* 91.3 tok/s 5.5s last 91.3–91.3 ✓ 1/1 │ +│ * predictable-output ceiling; not a proxy for agent speed │ +├─ CAPPED CONCURRENT GENERATION ───────────────────────────────────────────────────────────┤ +│ SHORT CODE • END-TO-END • 256-TOKEN CAP PER AGENT │ +│ AGGREGATE C1 47.0 • C2 60.5 • C3 66.8 • C4 70.9 tok/s │ +│ C4 OUTPUT STATE normal stop 0/8 • visible 8/8 • reasoning may be included │ +├─ STAGGERED ARRIVALS ─────────────────────────────────────────────────────────────────────┤ +│ S4 • 3 RUNNING + 1 ARRIVING • 32,768-TOKEN LONG REQUEST • 1024/256-TOKEN CAPS │ +│ DECODE-FIRST long TTFT 12.86s (1.03x solo) • incumbent stall 3.15s • 2/2 valid │ +│ PREFILL-FIRST short TTFT 3.54s (4.81x solo) • long TTFT 1.18x solo • 2/2 valid │ +├─ RECEIPT ────────────────────────────────────────────────────────────────────────────────┤ +│ JSON sha256:6a10d5b6d4c5ed43… │ +│ SHARE THE CARD • LINK THE JSON RECEIPT • #RIGMARK │ +│ github.com/alexellis/rigmark │ +╰──────────────────────────────────────────────────────────────────────────────────────────╯ diff --git a/results/2026-10-12-rc4/raw/rig-conc.json b/results/2026-10-12-rc4/raw/rig-conc.json new file mode 100644 index 0000000..258cff5 --- /dev/null +++ b/results/2026-10-12-rc4/raw/rig-conc.json @@ -0,0 +1,19106 @@ +{ + "concurrency": { + "1": { + "aggregate_end_to_end_tokens_per_second": { + "maximum": 47.624, + "median": 47.007, + "minimum": 46.389, + "p90": 47.624 + }, + "per_stream_decode_tokens_per_second": { + "maximum": 55.67, + "median": 54.334, + "minimum": 52.998, + "p90": 55.67 + }, + "per_stream_ttft_seconds": { + "maximum": 0.791, + "median": 0.748, + "minimum": 0.704, + "p90": 0.791 + }, + "rounds": [ + { + "aggregate_end_to_end_tokens_per_second": 46.389, + "completion_tokens": 256, + "round": 1, + "streams": [ + { + "completion_tokens": 256, + "decode_seconds": 4.811542, + "decode_tokens_per_second": 52.998, + "finish_reason": "length", + "measured_sse_events": 255, + "output": "```go\n// ratelimit.go\npackage ratelimit\n\nimport (\n\t\"errors\"\n\t\"math\"\n\t\"sync\"\n\t\"time\"\n)\n\n// Clock abstracts time for testability.\ntype Clock interface {\n\tNow() time.Time\n}\n\ntype realClock struct{}\n\nfunc (realClock) Now() time.Time { return time.Now() }\n\n// RealClock returns a Clock backed by the system time.\nfunc RealClock() Clock { return realClock{} }\n\n// Limiter is a concurrency-safe token-bucket rate limiter.\n// It is safe for concurrent use by multiple goroutines.\ntype Limiter struct {\n\tmu sync.Mutex\n\trate float64 // tokens per second\n\tburst float64 // bucket capacity\n\ttokens float64\n\tlastFill time.Time\n\tclock Clock\n}\n\n// NewLimiter creates a Limiter that refills at rate tokens per second,\n// with a maximum burst capacity. Both must be > 0.\nfunc NewLimiter(rate float64, burst int, clock Clock) (*Limiter, error) {\n\tif rate <= 0 || math.IsNaN(rate) || math.IsInf(rate, 0) {\n\t\treturn nil, errors.New(\"ratelimit:", + "output_characters": 941, + "output_sha256": "2a5d200be2c756664510ae1460f258366326f32c238efcb16008ce19d2ed8895", + "prompt_tokens": 131, + "reasoning_characters": 31, + "reasoning_sha256": "0b43e4ed336e57da5a7c92d31a4254d0a9a41023d9c266c81567fdeb7ede531d", + "response_tail_seconds": 0.002869, + "stream_done_marker": true, + "stream_sha256": "752edce8bf56e115015af9643445849428f5505fd1384550504a3a65344bf237", + "time_to_first_visible_seconds": 0.938838, + "time_to_last_output_seconds": 5.515132, + "ttft_seconds": 0.70359, + "wall_seconds": 5.518001 + } + ], + "wall_seconds": 5.518606 + }, + { + "aggregate_end_to_end_tokens_per_second": 47.624, + "completion_tokens": 256, + "round": 2, + "streams": [ + { + "completion_tokens": 256, + "decode_seconds": 4.580539, + "decode_tokens_per_second": 55.67, + "finish_reason": "length", + "measured_sse_events": 255, + "output": "```go\n// ratelimit.go\npackage ratelimit\n\nimport (\n\t\"errors\"\n\t\"fmt\"\n\t\"sync\"\n\t\"time\"\n)\n\n// Clock abstracts time for testability.\ntype Clock interface {\n\tNow() time.Time\n}\n\ntype realClock struct{}\n\nfunc (realClock) Now() time.Time { return time.Now() }\n\n// RealClock is the production clock.\nvar RealClock Clock = realClock{}\n\n// Limiter is a concurrency-safe token-bucket rate limiter.\n// A Limiter must be constructed with NewLimiter; the zero value is invalid.\ntype Limiter struct {\n\tmu sync.Mutex\n\tclock Clock\n\trate float64 // tokens per second\n\tburst float64 // bucket capacity\n\ttokens float64\n\tlastFill time.Time\n}\n\nvar (\n\tErrNonPositiveRate = errors.New(\"ratelimit: rate must be > 0\")\n\tErrNonPositiveBurst = errors.New(\"ratelimit: burst must be > 0\")\n)\n\n// NewLimiter creates a limiter refilling at rate tokens/second with\n// capacity burst. The bucket starts full.\nfunc NewLimiter(rate float64, burst int) (*Limiter, error) {\n\treturn New", + "output_characters": 967, + "output_sha256": "c4af34cee7d6f1f8d72eb95e5ef531e0ac20316d4ca98c11d636970c0f7aee7a", + "prompt_tokens": 134, + "reasoning_characters": 0, + "reasoning_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", + "response_tail_seconds": 0.002681, + "stream_done_marker": true, + "stream_sha256": "c4af34cee7d6f1f8d72eb95e5ef531e0ac20316d4ca98c11d636970c0f7aee7a", + "time_to_first_visible_seconds": 0.791489, + "time_to_last_output_seconds": 5.372027, + "ttft_seconds": 0.791489, + "wall_seconds": 5.374708 + } + ], + "wall_seconds": 5.375489 + } + ] + }, + "2": { + "aggregate_end_to_end_tokens_per_second": { + "maximum": 61.502, + "median": 60.504, + "minimum": 59.505, + "p90": 61.502 + }, + "per_stream_decode_tokens_per_second": { + "maximum": 40.099, + "median": 36.356, + "minimum": 32.315, + "p90": 40.099 + }, + "per_stream_ttft_seconds": { + "maximum": 1.498, + "median": 1.093, + "minimum": 0.699, + "p90": 1.498 + }, + "rounds": [ + { + "aggregate_end_to_end_tokens_per_second": 59.505, + "completion_tokens": 512, + "round": 1, + "streams": [ + { + "completion_tokens": 256, + "decode_seconds": 7.890969, + "decode_tokens_per_second": 32.315, + "finish_reason": "length", + "measured_sse_events": 255, + "output": "```go\n// ratelimit.go\npackage ratelimit\n\nimport (\n\t\"errors\"\n\t\"sync\"\n\t\"time\"\n)\n\n// Clock reports the current time. It is injectable for testing.\ntype Clock interface {\n\tNow() time.Time\n}\n\ntype systemClock struct{}\n\nfunc (systemClock) Now() time.Time { return time.Now() }\n\n// SystemClock is the default real-time clock.\nvar SystemClock Clock = systemClock{}\n\n// Limiter is a concurrency-safe token-bucket rate limiter.\n// The bucket holds at most burst tokens and is refilled at rate tokens/sec.\ntype Limiter struct {\n\tmu sync.Mutex\n\trate float64 // tokens per second\n\tburst float64 // bucket capacity\n\ttokens float64 // current tokens\n\tlastFill time.Time // last refill time\n\tclock Clock\n}\n\n// New creates a Limiter that admits at most rate events per second,\n// with an initial burst capacity of burst tokens.\nfunc New(rate, burst float64, clock Clock) (*Limiter, error) {\n\tif rate <= 0 {\n\t\treturn nil, errors.New(\"ratelimit: rate must be > 0\")\n\t}\n\tif burst ", + "output_characters": 983, + "output_sha256": "05ebc028bd2ee7c9bd53255e9b867457dd73a5f7b6f6450688acc247519e0451", + "prompt_tokens": 132, + "reasoning_characters": 31, + "reasoning_sha256": "0b43e4ed336e57da5a7c92d31a4254d0a9a41023d9c266c81567fdeb7ede531d", + "response_tail_seconds": 0.002797, + "stream_done_marker": true, + "stream_sha256": "62d0be70d56d9ce469ec06f2e9f97a3682b0525375ae2b6e96bc0d0193689339", + "time_to_first_visible_seconds": 1.745509, + "time_to_last_output_seconds": 8.600556, + "ttft_seconds": 0.709587, + "wall_seconds": 8.603353 + }, + { + "completion_tokens": 256, + "decode_seconds": 6.359215, + "decode_tokens_per_second": 40.099, + "finish_reason": "length", + "measured_sse_events": 255, + "output": "```go\n// ratelimit.go\npackage ratelimit\n\nimport (\n\t\"errors\"\n\t\"sync\"\n\t\"time\"\n)\n\n// Clock abstracts time for testability.\ntype Clock interface {\n\tNow() time.Time\n}\n\ntype realClock struct{}\n\nfunc (realClock) Now() time.Time { return time.Now() }\n\n// RealClock returns a Clock backed by the system clock.\nfunc RealClock() Clock { return realClock{} }\n\n// Limiter is a concurrency-safe token-bucket rate limiter.\n// The bucket holds at most Burst tokens and refills at Rate tokens per second.\ntype Limiter struct {\n\tmu sync.Mutex\n\trate float64 // tokens per second\n\tburst int\n\ttokens float64\n\tlastRef time.Time\n\tclock Clock\n}\n\n// New creates a Limiter with the given refill rate (tokens/sec) and burst size.\nfunc New(rate float64, burst int, clock Clock) (*Limiter, error) {\n\tif rate <= 0 {\n\t\treturn nil, errors.New(\"ratelimit: rate must be > 0\")\n\t}\n\tif burst <= 0 {\n\t\treturn nil, errors.New(\"ratelimit: burst must be > 0\")\n", + "output_characters": 932, + "output_sha256": "4ee3e65387551b1844e80274fb527e8ef8cb115d0eeffd34a8d967291c12b001", + "prompt_tokens": 130, + "reasoning_characters": 22, + "reasoning_sha256": "af6f043f26025000aeb3ba955cd4e01df3f98b893747d92a1b99562b764fc47e", + "response_tail_seconds": 0.006598, + "stream_done_marker": true, + "stream_sha256": "a19951f466cea49e4918d1803c721da93d746bac967c167fa096f7d340046751", + "time_to_first_visible_seconds": 1.746037, + "time_to_last_output_seconds": 7.857487, + "ttft_seconds": 1.498273, + "wall_seconds": 7.864085 + } + ], + "wall_seconds": 8.604363 + }, + { + "aggregate_end_to_end_tokens_per_second": 61.502, + "completion_tokens": 512, + "round": 2, + "streams": [ + { + "completion_tokens": 256, + "decode_seconds": 6.494955, + "decode_tokens_per_second": 39.261, + "finish_reason": "length", + "measured_sse_events": 255, + "output": "```go\n// ratelimit.go\npackage ratelimit\n\nimport (\n\t\"errors\"\n\t\"fmt\"\n\t\"sync\"\n\t\"time\"\n)\n\n// Clock abstracts time for testability.\ntype Clock interface {\n\tNow() time.Time\n}\n\ntype realClock struct{}\n\nfunc (realClock) Now() time.Time { return time.Now() }\n\n// RealClock returns a Clock backed by the system clock.\nfunc RealClock() Clock { return realClock{} }\n\n// Limiter is a concurrency-safe token-bucket rate limiter.\n// Tokens refill at rate tokens per second, up to capacity burst.\ntype Limiter struct {\n\tmu sync.Mutex\n\tcapacity int\n\trate float64 // tokens per second\n\ttokens float64\n\tlast time.Time\n\tclock Clock\n}\n\n// NewLimiter creates a Limiter with a bucket of capacity burst,\n// refilled at rate tokens per second. The bucket starts full.\nfunc NewLimiter(rate float64, burst int, clock Clock) (*Limiter, error) {\n\tif rate <= 0 {\n\t\treturn nil, fmt.Errorf(\"rate must be positive, got %v\", rate)\n\t}\n\tif burst <= 0 {\n\t\treturn nil, fmt.Errorf(\"burst must be positive", + "output_characters": 984, + "output_sha256": "2c6216039966267742cdcb01a56111d7f7f4731c6e4fb948deeb792f5313204d", + "prompt_tokens": 133, + "reasoning_characters": 0, + "reasoning_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", + "response_tail_seconds": 0.007009, + "stream_done_marker": true, + "stream_sha256": "2c6216039966267742cdcb01a56111d7f7f4731c6e4fb948deeb792f5313204d", + "time_to_first_visible_seconds": 1.476933, + "time_to_last_output_seconds": 7.971888, + "ttft_seconds": 1.476933, + "wall_seconds": 7.978897 + }, + { + "completion_tokens": 256, + "decode_seconds": 7.622803, + "decode_tokens_per_second": 33.452, + "finish_reason": "length", + "measured_sse_events": 255, + "output": "**limiter.go**\n```go\n// Package ratelimit provides a concurrency-safe token-bucket rate limiter.\npackage ratelimit\n\nimport (\n\t\"errors\"\n\t\"fmt\"\n\t\"sync\"\n\t\"time\"\n)\n\n// Clock reports the current time. It is an injection point for tests.\ntype Clock interface {\n\tNow() time.Time\n}\n\ntype realClock struct{}\n\nfunc (realClock) Now() time.Time { return time.Now() }\n\n// Limiter is a concurrency-safe token-bucket rate limiter.\n// A Limiter must be constructed with New; the zero value is not usable.\ntype Limiter struct {\n\tmu sync.Mutex\n\trate float64 // tokens per second\n\tburst float64 // bucket capacity\n\ttokens float64 // current tokens\n\tlast time.Time // last refill time\n\tclock Clock\n}\n\n// New creates a Limiter that admits events at rate tokens per second,\n// with a maximum burst size of burst tokens.\nfunc New(rate float64, burst int, clock Clock) (*Limiter, error) {\n\tif rate <= 0 {\n\t\treturn nil, fmt.Errorf(\"rate must be positive, got %v\", rate)\n", + "output_characters": 973, + "output_sha256": "2f8081776cdb39997e776912a314e99c71735d7704db45b530295d9e40451c3d", + "prompt_tokens": 130, + "reasoning_characters": 37, + "reasoning_sha256": "a5635dea4d8b0256ccd62004069db4f571c1ecc9dc4fb1d0b2f87c5e1c1e759d", + "response_tail_seconds": 0.002564, + "stream_done_marker": true, + "stream_sha256": "7f268de02ae1bb2c56b3b58acd226a53c1540405425c915ec5c0df6d293bd564", + "time_to_first_visible_seconds": 1.717432, + "time_to_last_output_seconds": 8.321558, + "ttft_seconds": 0.698755, + "wall_seconds": 8.324121 + } + ], + "wall_seconds": 8.324954 + } + ] + }, + "3": { + "aggregate_end_to_end_tokens_per_second": { + "maximum": 68.039, + "median": 66.757, + "minimum": 65.476, + "p90": 68.039 + }, + "per_stream_decode_tokens_per_second": { + "maximum": 28.798, + "median": 27.195, + "minimum": 23.913, + "p90": 28.622 + }, + "per_stream_ttft_seconds": { + "maximum": 2.374, + "median": 1.952, + "minimum": 0.729, + "p90": 2.351 + }, + "rounds": [ + { + "aggregate_end_to_end_tokens_per_second": 65.476, + "completion_tokens": 768, + "round": 1, + "streams": [ + { + "completion_tokens": 256, + "decode_seconds": 9.374076, + "decode_tokens_per_second": 27.203, + "finish_reason": "length", + "measured_sse_events": 255, + "output": "```go\n// ratelimit.go\npackage ratelimit\n\nimport (\n\t\"errors\"\n\t\"fmt\"\n\t\"sync\"\n\t\"time\"\n)\n\n// Clock abstracts time for testability.\ntype Clock interface {\n\tNow() time.Time\n}\n\ntype realClock struct{}\n\nfunc (realClock) Now() time.Time { return time.Now() }\n\n// Limiter is a concurrency-safe token-bucket rate limiter.\n// Tokens refill continuously at rate TokensPerInterval per Interval.\ntype Limiter struct {\n\tmu sync.Mutex\n\n\ttokens float64\n\tmaxTokens float64\n\trefillRate float64 // tokens per second\n\tlastRefill time.Time\n\tinterval time.Duration\n\ttokensPerInt int64\n\tclock Clock\n}\n\nvar ErrInvalidConfig = errors.New(\"ratelimit: invalid configuration\")\n\n// New creates a Limiter allowing tokensPerInt tokens per interval, with a\n// burst capacity of burst tokens. burst must be >= tokensPerInt.\nfunc New(tokensPerInt int64, interval time.Duration, burst int64, opts ...Option) (*Limiter, error) {\n\tif tokensPerInt <= 0 {\n\t\treturn nil, fmt.Errorf(\"%w: tokensPerInt must be > 0\", ErrInvalid", + "output_characters": 1006, + "output_sha256": "2c531234accac3507ba37c03354f3c1c210172d7a3740196a22956cf0496491b", + "prompt_tokens": 129, + "reasoning_characters": 0, + "reasoning_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", + "response_tail_seconds": 0.002719, + "stream_done_marker": true, + "stream_sha256": "2c531234accac3507ba37c03354f3c1c210172d7a3740196a22956cf0496491b", + "time_to_first_visible_seconds": 2.351387, + "time_to_last_output_seconds": 11.725463, + "ttft_seconds": 2.351387, + "wall_seconds": 11.728182 + }, + { + "completion_tokens": 256, + "decode_seconds": 8.854762, + "decode_tokens_per_second": 28.798, + "finish_reason": "length", + "measured_sse_events": 255, + "output": "```go\n// ratelimit.go\npackage ratelimit\n\nimport (\n\t\"errors\"\n\t\"sync\"\n\t\"time\"\n)\n\nvar ErrInvalidLimit = errors.New(\"ratelimit: limit must be > 0 and burst must be >= 0\")\n\n// Clock abstracts time for testability.\ntype Clock interface {\n\tNow() time.Time\n}\n\ntype realClock struct{}\n\nfunc (realClock) Now() time.Time { return time.Now() }\n\n// RealClock returns a Clock backed by the system clock.\nfunc RealClock() Clock { return realClock{} }\n\n// Limiter is a concurrency-safe token-bucket rate limiter.\n// A Limiter must be created by NewLimiter and is safe for concurrent use.\ntype Limiter struct {\n\tmu sync.Mutex\n\tlimit float64 // tokens per second\n\tburst float64 // bucket capacity\n\ttokens float64 // current tokens\n\tlast time.Time // last refill time\n\tclock Clock\n}\n\n// NewLimiter creates a Limiter allowing limit events per second with burst\n// capacity. The bucket starts full.\nfunc NewLimiter(limit float64, burst int, clock Clock) (*Limiter, error) {\n\tif limit <= 0 ||", + "output_characters": 1013, + "output_sha256": "45b8b443adab0b0f51259bf6f06b3054b232f4eaa2f4a7338baddbca626e903e", + "prompt_tokens": 135, + "reasoning_characters": 0, + "reasoning_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", + "response_tail_seconds": 0.009199, + "stream_done_marker": true, + "stream_sha256": "45b8b443adab0b0f51259bf6f06b3054b232f4eaa2f4a7338baddbca626e903e", + "time_to_first_visible_seconds": 2.350794, + "time_to_last_output_seconds": 11.205556, + "ttft_seconds": 2.350794, + "wall_seconds": 11.214755 + }, + { + "completion_tokens": 256, + "decode_seconds": 10.663451, + "decode_tokens_per_second": 23.913, + "finish_reason": "length", + "measured_sse_events": 255, + "output": "```go\n// ratelimit.go\npackage ratelimit\n\nimport (\n\t\"errors\"\n\t\"fmt\"\n\t\"sync\"\n\t\"time\"\n)\n\n// Clock abstracts time for testability.\ntype Clock interface {\n\tNow() time.Time\n}\n\n// SystemClock is the production Clock.\ntype SystemClock struct{}\n\n// Now returns the current wall-clock time.\nfunc (SystemClock) Now() time.Time { return time.Now() }\n\n// Limiter is a concurrency-safe token-bucket rate limiter.\n// A Limiter must be constructed via NewLimiter; the zero value is invalid.\ntype Limiter struct {\n\tmu sync.Mutex\n\tclock Clock\n\trate float64 // tokens per second\n\tburst float64 // bucket capacity\n\ttokens float64\n\tlast time.Time\n\tlastValid bool\n}\n\n// ErrInvalidConfig is returned by NewLimiter for non-positive rate or burst.\nvar ErrInvalidConfig = errors.New(\"ratelimit: rate and burst must be > 0\")\n\n// NewLimiter creates a Limiter refilling at rate tokens per second with a\n// maximum capacity of burst tokens. The bucket starts full.\nfunc NewLimiter(clock Clock, rate, burst float64) (*Limiter, error)", + "output_characters": 1030, + "output_sha256": "cc104eb642fcaff87fc31694323234381dcf68457a45e768372947eee89d1814", + "prompt_tokens": 132, + "reasoning_characters": 0, + "reasoning_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", + "response_tail_seconds": 0.007269, + "stream_done_marker": true, + "stream_sha256": "cc104eb642fcaff87fc31694323234381dcf68457a45e768372947eee89d1814", + "time_to_first_visible_seconds": 0.784178, + "time_to_last_output_seconds": 11.447629, + "ttft_seconds": 0.784178, + "wall_seconds": 11.454898 + } + ], + "wall_seconds": 11.729518 + }, + { + "aggregate_end_to_end_tokens_per_second": 68.039, + "completion_tokens": 768, + "round": 2, + "streams": [ + { + "completion_tokens": 256, + "decode_seconds": 10.460342, + "decode_tokens_per_second": 24.378, + "finish_reason": "length", + "measured_sse_events": 255, + "output": "```go\n// ratelimit.go\npackage ratelimit\n\nimport (\n\t\"errors\"\n\t\"sync\"\n\t\"time\"\n)\n\n// Clock abstracts time for testability.\ntype Clock interface {\n\tNow() time.Time\n}\n\ntype realClock struct{}\n\nfunc (realClock) Now() time.Time { return time.Now() }\n\n// Limiter is a concurrency-safe token-bucket rate limiter.\n// Tokens refill continuously at rate Rate() per second, up to Burst.\ntype Limiter struct {\n\tmu sync.Mutex\n\ttokens float64\n\tlast time.Time\n\tburst int\n\trate float64 // tokens per second\n\tclock Clock\n}\n\n// New creates a Limiter with the given burst size and refill rate\n// (tokens per second). burst must be > 0 and rate must be > 0.\nfunc New(burst int, rate float64) (*Limiter, error) {\n\treturn NewWithClock(burst, rate, realClock{})\n}\n\n// NewWithClock is New with an injectable Clock.\nfunc NewWithClock(burst int, rate float64, clock Clock) (*Limiter, error) {\n\tif burst <= 0 {\n\t\treturn nil, errors.New(\"", + "output_characters": 918, + "output_sha256": "e0e0e2740d834f14394d53775f4169ded6e0de64f22683bb2816f72173457d09", + "prompt_tokens": 131, + "reasoning_characters": 31, + "reasoning_sha256": "0b43e4ed336e57da5a7c92d31a4254d0a9a41023d9c266c81567fdeb7ede531d", + "response_tail_seconds": 0.007008, + "stream_done_marker": true, + "stream_sha256": "421927f1d1d5739cdc1a6e0b02b695afff0e2d69900fe8d87496e86837885ba6", + "time_to_first_visible_seconds": 2.559544, + "time_to_last_output_seconds": 11.189595, + "ttft_seconds": 0.729253, + "wall_seconds": 11.196603 + }, + { + "completion_tokens": 256, + "decode_seconds": 8.90914, + "decode_tokens_per_second": 28.622, + "finish_reason": "length", + "measured_sse_events": 255, + "output": "**ratelimit.go**\n```go\n// Package ratelimit implements a concurrency-safe token-bucket rate limiter.\npackage ratelimit\n\nimport (\n\t\"errors\"\n\t\"math\"\n\t\"sync\"\n\t\"time\"\n)\n\n// Clock reports the current time. It is an interface so tests can inject a\n// fake clock.\ntype Clock interface {\n\tNow() time.Time\n}\n\ntype systemClock struct{}\n\nfunc (systemClock) Now() time.Time { return time.Now() }\n\n// SystemClock is the default Clock backed by time.Now.\nvar SystemClock Clock = systemClock{}\n\n// Limiter is a concurrency-safe token-bucket rate limiter. The zero value is\n// not usable; construct one with New.\ntype Limiter struct {\n\tmu sync.Mutex\n\trate float64 // tokens per second\n\tburst int // bucket capacity\n\ttokens float64 // current tokens\n\tlast time.Time\n\tclock Clock\n}\n\n// ErrInvalidRate is returned by New when rate is not positive and finite.\nvar ErrInvalidRate = errors.New(\"ratelimit: rate must be > 0 and finite\")\n\n// ErrInvalidBurst is returned by New when burst is not positive.\nvar ErrInvalidBurst = errors.New(\"", + "output_characters": 1041, + "output_sha256": "24a53a7a279ce8404f8a00914529b5a1fbc60fa5e272246475c40a38af991b74", + "prompt_tokens": 133, + "reasoning_characters": 0, + "reasoning_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", + "response_tail_seconds": 0.002819, + "stream_done_marker": true, + "stream_sha256": "24a53a7a279ce8404f8a00914529b5a1fbc60fa5e272246475c40a38af991b74", + "time_to_first_visible_seconds": 2.373834, + "time_to_last_output_seconds": 11.282974, + "ttft_seconds": 2.373834, + "wall_seconds": 11.285793 + }, + { + "completion_tokens": 256, + "decode_seconds": 9.378998, + "decode_tokens_per_second": 27.188, + "finish_reason": "length", + "measured_sse_events": 255, + "output": "```go\n// ratelimit.go\npackage ratelimit\n\nimport (\n\t\"errors\"\n\t\"sync\"\n\t\"time\"\n)\n\n// Clock provides the current time; injectable for testing.\ntype Clock interface {\n\tNow() time.Time\n}\n\ntype realClock struct{}\n\nfunc (realClock) Now() time.Time { return time.Now() }\n\n// Limiter is a concurrency-safe token-bucket rate limiter.\n// Tokens refill at rate tokens per second, up to capacity Burst.\ntype Limiter struct {\n\tmu sync.Mutex\n\tburst int\n\trate float64 // tokens per second\n\ttokens float64\n\tlastRefill time.Time\n\tclock Clock\n}\n\nvar ErrInvalidConfig = errors.New(\"ratelimit: burst must be > 0 and rate must be > 0\")\n\n// New creates a Limiter with the given burst capacity and refill rate\n// (tokens per second). A nil clock uses the real time.\nfunc New(burst int, rate float64, clock Clock) (*Limiter, error) {\n\tif burst <= 0 || rate <= 0 {\n\t\treturn nil, ErrInvalidConfig\n\t}\n\tif clock == nil {\n\t\tclock = realClock{}\n\t", + "output_characters": 932, + "output_sha256": "529b593d1e531530dd8e98bb56433609c6fd536b048a8382297328c97a2c3f73", + "prompt_tokens": 134, + "reasoning_characters": 31, + "reasoning_sha256": "0b43e4ed336e57da5a7c92d31a4254d0a9a41023d9c266c81567fdeb7ede531d", + "response_tail_seconds": 0.009062, + "stream_done_marker": true, + "stream_sha256": "66a3ce1948061c8581dc0414ee4bd75469f381a4cdb4fbcf84bfd82917c46681", + "time_to_first_visible_seconds": 2.666226, + "time_to_last_output_seconds": 10.931334, + "ttft_seconds": 1.552336, + "wall_seconds": 10.940396 + } + ], + "wall_seconds": 11.287616 + } + ] + }, + "4": { + "aggregate_end_to_end_tokens_per_second": { + "maximum": 73.762, + "median": 70.865, + "minimum": 67.969, + "p90": 73.762 + }, + "per_stream_decode_tokens_per_second": { + "maximum": 23.468, + "median": 21.84, + "minimum": 19.43, + "p90": 23.215 + }, + "per_stream_ttft_seconds": { + "maximum": 3.204, + "median": 2.203, + "minimum": 1.446, + "p90": 3.075 + }, + "rounds": [ + { + "aggregate_end_to_end_tokens_per_second": 67.969, + "completion_tokens": 1024, + "round": 1, + "streams": [ + { + "completion_tokens": 256, + "decode_seconds": 11.563689, + "decode_tokens_per_second": 22.052, + "finish_reason": "length", + "measured_sse_events": 255, + "output": "```go\n// ratelimit.go\npackage ratelimit\n\nimport (\n\t\"errors\"\n\t\"sync\"\n\t\"time\"\n)\n\n// Clock reports the current time. Implementations must return monotonically\n// non-decreasing values for correct limiter behavior.\ntype Clock interface {\n\tNow() time.Time\n}\n\ntype realClock struct{}\n\nfunc (realClock) Now() time.Time { return time.Now() }\n\n// RealClock returns a Clock backed by time.Now.\nfunc RealClock() Clock { return realClock{} }\n\n// Limiter is a concurrency-safe token-bucket rate limiter.\n// Tokens refill continuously at rate RefillPerSecond; capacity is Burst.\ntype Limiter struct {\n\tmu sync.Mutex\n\tburst int // max tokens\n\ttokens float64 // current tokens\n\trate float64 // tokens per second\n\tlast time.Time // last refill time\n\tclock Clock\n}\n\nvar (\n\tErrNonPositiveRate = errors.New(\"ratelimit: rate must be > 0\")\n\tErrNonPositiveBurst = errors.New(\"ratelimit: burst must be > 0\")\n\tErrNilClock = errors.New(\"ratelimit: clock", + "output_characters": 984, + "output_sha256": "c96ee26d7c17ed627ff0a6aae64dc3b84566a63c657339955cc957c9f8ce7495", + "prompt_tokens": 133, + "reasoning_characters": 31, + "reasoning_sha256": "0b43e4ed336e57da5a7c92d31a4254d0a9a41023d9c266c81567fdeb7ede531d", + "response_tail_seconds": 0.010122, + "stream_done_marker": true, + "stream_sha256": "76098599ce219125f829dbafee7f54df437cdb42eadd15d812a28b1538e0fd2a", + "time_to_first_visible_seconds": 3.586761, + "time_to_last_output_seconds": 14.638447, + "ttft_seconds": 3.074758, + "wall_seconds": 14.648569 + }, + { + "completion_tokens": 256, + "decode_seconds": 13.124016, + "decode_tokens_per_second": 19.43, + "finish_reason": "length", + "measured_sse_events": 255, + "output": "```go\n// ratelimit.go\npackage ratelimit\n\nimport (\n\t\"errors\"\n\t\"sync\"\n\t\"time\"\n)\n\n// Clock abstracts time for testability.\ntype Clock interface {\n\tNow() time.Time\n}\n\ntype realClock struct{}\n\nfunc (realClock) Now() time.Time { return time.Now() }\n\n// RealClock returns a Clock backed by the system time.\nfunc RealClock() Clock { return realClock{} }\n\n// Limiter is a concurrency-safe token-bucket rate limiter.\n// A Limiter must be constructed via NewLimiter; the zero value is not usable.\ntype Limiter struct {\n\tmu sync.Mutex\n\trate float64 // tokens per second\n\tburst float64 // bucket capacity\n\ttokens float64 // available tokens\n\tlast time.Time // last refill time\n\tclock Clock\n\tnowFn func() time.Time // set only in tests via WithClock\n}\n\n// NewLimiter creates a Limiter refilling at rate tokens per second,\n// with a maximum burst capacity. Both must be > 0.\nfunc NewLimiter(rate, burst float64, clock Clock) (*Limiter, error) {\n\tif rate <= 0 {\n\t\treturn nil, errors", + "output_characters": 997, + "output_sha256": "78729a8079e8666b216d4372fc9e00005ab74844f52e9fe3d6f91e9fb769d047", + "prompt_tokens": 131, + "reasoning_characters": 0, + "reasoning_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", + "response_tail_seconds": 0.008696, + "stream_done_marker": true, + "stream_sha256": "78729a8079e8666b216d4372fc9e00005ab74844f52e9fe3d6f91e9fb769d047", + "time_to_first_visible_seconds": 1.513876, + "time_to_last_output_seconds": 14.637892, + "ttft_seconds": 1.513876, + "wall_seconds": 14.646589 + }, + { + "completion_tokens": 256, + "decode_seconds": 11.78975, + "decode_tokens_per_second": 21.629, + "finish_reason": "length", + "measured_sse_events": 255, + "output": "**ratelimit.go**\n```go\n// Package ratelimit implements a concurrency-safe token-bucket rate limiter.\npackage ratelimit\n\nimport (\n\t\"errors\"\n\t\"sync\"\n\t\"time\"\n)\n\n// Clock abstracts time for testability.\ntype Clock interface {\n\tNow() time.Time\n}\n\ntype realClock struct{}\n\nfunc (realClock) Now() time.Time { return time.Now() }\n\n// RealClock returns a Clock backed by the system time.\nfunc RealClock() Clock { return realClock{} }\n\n// Limiter is a token-bucket rate limiter. It is safe for concurrent use.\ntype Limiter struct {\n\tmu sync.Mutex\n\trate float64 // tokens per second\n\tburst float64 // bucket capacity\n\ttokens float64 // current tokens\n\tlast time.Time // last refill time\n\tclock Clock\n}\n\n// New creates a Limiter that refills rate tokens per second, up to burst.\nfunc New(rate float64, burst int, clock Clock) (*Limiter, error) {\n\tif rate <= 0 {\n\t\treturn nil, errors.New(\"ratelimit: rate must be > 0\")\n\t}\n\tif burst <= 0 {\n\t", + "output_characters": 955, + "output_sha256": "cfeb5163af87f0f87a6118c60242859d242dbb74bd1ca331e52c5d4196a41ee2", + "prompt_tokens": 134, + "reasoning_characters": 22, + "reasoning_sha256": "af6f043f26025000aeb3ba955cd4e01df3f98b893747d92a1b99562b764fc47e", + "response_tail_seconds": 0.010055, + "stream_done_marker": true, + "stream_sha256": "e9baa646387ac7a50ce280a1dbdaf28910e37714ec459566a737aa6466beb5b1", + "time_to_first_visible_seconds": 3.322936, + "time_to_last_output_seconds": 13.235866, + "ttft_seconds": 1.446116, + "wall_seconds": 13.245921 + }, + { + "completion_tokens": 256, + "decode_seconds": 11.857993, + "decode_tokens_per_second": 21.504, + "finish_reason": "length", + "measured_sse_events": 255, + "output": "```ratelimit.go\n// Package ratelimit implements a concurrency-safe token-bucket rate limiter.\npackage ratelimit\n\nimport (\n\t\"errors\"\n\t\"math\"\n\t\"sync\"\n\t\"time\"\n)\n\n// Clock reports the current time. It is an injectable abstraction over time.Now.\ntype Clock interface {\n\tNow() time.Time\n}\n\n// SystemClock is the default Clock backed by time.Now.\ntype SystemClock struct{}\n\n// Now returns the current wall-clock time.\nfunc (SystemClock) Now() time.Time { return time.Now() }\n\n// Limiter is a concurrency-safe token-bucket rate limiter.\n// The zero value is not usable; construct with New.\ntype Limiter struct {\n\tmu sync.Mutex\n\ttokens float64\n\tlast time.Time\n\trate float64 // tokens per second\n\tburst float64 // bucket capacity\n\tmaxBurst int // integer burst for AllowN validation\n\tclock Clock\n}\n\n// ErrInvalidConfig is returned by New when rate or burst are not positive.\nvar ErrInvalidConfig = errors.New(\"ratelimit: rate and burst must be > 0\")\n\n// New returns a Limiter that refills at rate tokens per second with a\n// maximum burst capacity of", + "output_characters": 1066, + "output_sha256": "75030c465eae137436cb1d6acf1e69d085302468a560a96035fac54f9f251de6", + "prompt_tokens": 131, + "reasoning_characters": 0, + "reasoning_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", + "response_tail_seconds": 0.002151, + "stream_done_marker": true, + "stream_sha256": "75030c465eae137436cb1d6acf1e69d085302468a560a96035fac54f9f251de6", + "time_to_first_visible_seconds": 3.203816, + "time_to_last_output_seconds": 15.06181, + "ttft_seconds": 3.203816, + "wall_seconds": 15.06396 + } + ], + "wall_seconds": 15.065647 + }, + { + "aggregate_end_to_end_tokens_per_second": 73.762, + "completion_tokens": 1024, + "round": 2, + "streams": [ + { + "completion_tokens": 256, + "decode_seconds": 10.865697, + "decode_tokens_per_second": 23.468, + "finish_reason": "length", + "measured_sse_events": 255, + "output": "**ratelimit.go**\n```go\n// Package ratelimit implements a concurrency-safe token-bucket rate limiter.\npackage ratelimit\n\nimport (\n\t\"errors\"\n\t\"sync\"\n\t\"time\"\n)\n\n// Clock abstracts time for testability.\ntype Clock interface {\n\tNow() time.Time\n}\n\ntype realClock struct{}\n\nfunc (realClock) Now() time.Time { return time.Now() }\n\n// RealClock returns a Clock backed by the system clock.\nfunc RealClock() Clock { return realClock{} }\n\n// Limiter is a concurrency-safe token-bucket rate limiter.\n// A zero-value Limiter is not usable; use New.\ntype Limiter struct {\n\tmu sync.Mutex\n\tclock Clock\n\trate float64 // tokens per second\n\tburst float64 // bucket capacity\n\ttokens float64 // current tokens\n\tlast time.Time // last refill time\n}\n\n// ErrInvalidConfig is returned by New for non-positive rate or burst.\nvar ErrInvalidConfig = errors.New(\"ratelimit: rate and burst must be > 0\")\n\n// New returns a Limiter that refills at rate tokens per second, up to burst.\nfunc New(rate float64, burst int, clock Clock)", + "output_characters": 1032, + "output_sha256": "2971874619c617b2f3bec1a73ebbfbb43639e7a35239c3f0f07a57f0f8e23e7c", + "prompt_tokens": 131, + "reasoning_characters": 0, + "reasoning_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", + "response_tail_seconds": 0.005311, + "stream_done_marker": true, + "stream_sha256": "2971874619c617b2f3bec1a73ebbfbb43639e7a35239c3f0f07a57f0f8e23e7c", + "time_to_first_visible_seconds": 3.0103, + "time_to_last_output_seconds": 13.875997, + "ttft_seconds": 3.0103, + "wall_seconds": 13.881309 + }, + { + "completion_tokens": 256, + "decode_seconds": 10.984407, + "decode_tokens_per_second": 23.215, + "finish_reason": "length", + "measured_sse_events": 255, + "output": "```go\n// ratelimit.go\npackage ratelimit\n\nimport (\n\t\"errors\"\n\t\"sync\"\n\t\"time\"\n)\n\n// Clock reports the current time. It is an injection point for tests.\ntype Clock interface {\n\tNow() time.Time\n}\n\ntype systemClock struct{}\n\nfunc (systemClock) Now() time.Time { return time.Now() }\n\n// SystemClock is the default Clock backed by time.Now.\nvar SystemClock Clock = systemClock{}\n\n// Limiter is a concurrency-safe token-bucket rate limiter.\n// The bucket starts full with capacity tokens and refills at rate tokens\n// per second. All methods are safe for concurrent use.\ntype Limiter struct {\n\tmu sync.Mutex\n\ttokens float64\n\tcapacity float64\n\trate float64 // tokens per second\n\tlast time.Time\n\tclock Clock\n}\n\n// NewLimiter creates a Limiter with the given burst capacity and refill\n// rate (tokens per second). capacity must be > 0 and rate must be > 0.\nfunc NewLimiter(capacity int, rate float64) (*Limiter, error) {\n\treturn NewLimiterWithClock(capacity, rate, SystemClock)\n}\n\n// NewLimiter", + "output_characters": 1002, + "output_sha256": "cc05e3575c611f4a99e030ce465f4c95453a350975c72850e768a07dddf8a49e", + "prompt_tokens": 132, + "reasoning_characters": 19, + "reasoning_sha256": "aeeca6ef7d56cc8b81124ee64ca083786bfc106c6fc57c0800bbce37c752dcc2", + "response_tail_seconds": 0.003314, + "stream_done_marker": true, + "stream_sha256": "578cb960d567b12c833523543a196c9511de1b12a94b25df60208363b41632d6", + "time_to_first_visible_seconds": 3.225595, + "time_to_last_output_seconds": 13.875974, + "ttft_seconds": 2.891567, + "wall_seconds": 13.879288 + }, + { + "completion_tokens": 256, + "decode_seconds": 12.068729, + "decode_tokens_per_second": 21.129, + "finish_reason": "length", + "measured_sse_events": 255, + "output": "```go\n// ratelimit.go\npackage ratelimit\n\nimport (\n\t\"errors\"\n\t\"fmt\"\n\t\"sync\"\n\t\"time\"\n)\n\n// Clock abstracts time for testability.\ntype Clock interface {\n\tNow() time.Time\n}\n\ntype realClock struct{}\n\nfunc (realClock) Now() time.Time { return time.Now() }\n\n// RealClock returns a Clock backed by time.Now.\nfunc RealClock() Clock { return realClock{} }\n\n// Limiter is a concurrency-safe token-bucket rate limiter.\n// Tokens refill continuously at rate tokens per second, up to Burst.\ntype Limiter struct {\n\tmu sync.Mutex\n\ttokens float64\n\tlast time.Time\n\trate float64 // tokens per second\n\tburst float64\n\tmaxBurst float64\n\tclock Clock\n}\n\n// NewLimiter creates a Limiter allowing rate events per second with burst\n// capacity. rate must be > 0 and burst must be >= 1.\nfunc NewLimiter(rate float64, burst int) (*Limiter, error) {\n\treturn NewLimiterWithClock(rate, burst, RealClock())\n}\n\n// NewLimiterWithClock is NewLimiter with an injectable Clock.\nfunc NewLimiterWithClock", + "output_characters": 986, + "output_sha256": "49821cd9b4add22446017816529f51bb4fd357f4895de436f162fdc93a956946", + "prompt_tokens": 131, + "reasoning_characters": 0, + "reasoning_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", + "response_tail_seconds": 0.009161, + "stream_done_marker": true, + "stream_sha256": "49821cd9b4add22446017816529f51bb4fd357f4895de436f162fdc93a956946", + "time_to_first_visible_seconds": 1.478397, + "time_to_last_output_seconds": 13.547126, + "ttft_seconds": 1.478397, + "wall_seconds": 13.556287 + }, + { + "completion_tokens": 256, + "decode_seconds": 11.173428, + "decode_tokens_per_second": 22.822, + "finish_reason": "length", + "measured_sse_events": 255, + "output": "```go\n// ratelimit.go\npackage ratelimit\n\nimport (\n\t\"errors\"\n\t\"sync\"\n\t\"time\"\n)\n\n// Clock abstracts time for testability.\ntype Clock interface {\n\tNow() time.Time\n}\n\ntype realClock struct{}\n\nfunc (realClock) Now() time.Time { return time.Now() }\n\n// RealClock returns a Clock backed by the system time.\nfunc RealClock() Clock { return realClock{} }\n\n// Limiter is a concurrency-safe token-bucket rate limiter.\ntype Limiter struct {\n\tmu sync.Mutex\n\trate float64 // tokens per second\n\tburst float64 // bucket capacity\n\ttokens float64 // current tokens\n\tlast time.Time // last refill time\n\tclock Clock\n}\n\nvar (\n\tErrNonPositiveRate = errors.New(\"ratelimit: rate must be > 0\")\n\tErrNonPositiveBurst = errors.New(\"ratelimit: burst must be > 0\")\n\tErrNilClock = errors.New(\"ratelimit: clock must not be nil\")\n)\n\n// New creates a Limiter with the given refill rate (tokens/sec), burst\n// capacity, and clock. The bucket starts full.\nfunc New(rate float", + "output_characters": 982, + "output_sha256": "d711bd397be6c36a0703904aad4b9bd5a11d5626f436a099727f5be87653ad45", + "prompt_tokens": 135, + "reasoning_characters": 0, + "reasoning_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", + "response_tail_seconds": 0.011072, + "stream_done_marker": true, + "stream_sha256": "d711bd397be6c36a0703904aad4b9bd5a11d5626f436a099727f5be87653ad45", + "time_to_first_visible_seconds": 1.479179, + "time_to_last_output_seconds": 12.652607, + "ttft_seconds": 1.479179, + "wall_seconds": 12.663678 + } + ], + "wall_seconds": 13.882506 + } + ] + } + }, + "decode": { + "code": { + "completion_gate": { + "passed": 1, + "total": 1 + }, + "decode_tokens_per_second": { + "maximum": 68.547, + "median": 68.547, + "minimum": 68.547, + "p90": 68.547 + }, + "runs": [ + { + "completion_tokens": 2172, + "completion_validation": { + "valid": true + }, + "decode_seconds": 31.671559, + "decode_tokens_per_second": 68.547, + "finish_reason": "stop", + "measured_sse_events": 2170, + "output": "```go\n// ratelimit.go\npackage ratelimit\n\nimport (\n\t\"errors\"\n\t\"fmt\"\n\t\"sync\"\n\t\"time\"\n)\n\n// Clock abstracts time for testability.\ntype Clock interface {\n\tNow() time.Time\n}\n\n// realClock uses the standard library.\ntype realClock struct{}\n\nfunc (realClock) Now() time.Time { return time.Now() }\n\n// Limiter is a concurrency-safe token-bucket rate limiter.\n// A zero value is not usable; construct with NewLimiter.\ntype Limiter struct {\n\tmu sync.Mutex\n\trate float64 // tokens per second\n\tburst float64 // bucket capacity\n\ttokens float64 // current tokens\n\tlast time.Time // last refill time\n\tclock Clock\n}\n\n// NewLimiter creates a limiter that refills rate tokens per second,\n// with a maximum capacity of burst tokens.\nfunc NewLimiter(rate float64, burst int) (*Limiter, error) {\n\treturn NewLimiterWithClock(rate, burst, realClock{})\n}\n\n// NewLimiterWithClock is like NewLimiter with an injectable clock.\nfunc NewLimiterWithClock(rate float64, burst int, clock Clock) (*Limiter, error) {\n\tif clock == nil {\n\t\treturn nil, errors.New(\"ratelimit: nil clock\")\n\t}\n\tif rate <= 0 {\n\t\treturn nil, fmt.Errorf(\"ratelimit: rate must be > 0, got %v\", rate)\n\t}\n\tif burst <= 0 {\n\t\treturn nil, fmt.Errorf(\"ratelimit: burst must be > 0, got %d\", burst)\n\t}\n\treturn &Limiter{\n\t\trate: rate,\n\t\tburst: float64(burst),\n\t\ttokens: float64(burst), // start full\n\t\tlast: clock.Now(),\n\t\tclock: clock,\n\t}, nil\n}\n\n// advance refills tokens based on elapsed time. Caller must hold mu.\nfunc (l *Limiter) advance(now time.Time) {\n\tif now.After(l.last) {\n\t\telapsed := now.Sub(l.last).Seconds()\n\t\tl.tokens = min(l.burst, l.tokens+elapsed*l.rate)\n\t\tl.last = now\n\t}\n}\n\n// AllowN reports whether n tokens may be consumed at time now.\n// If allowed, the tokens are consumed.\nfunc (l *Limiter) AllowN(now time.Time, n int) bool {\n\tif n <= 0 {\n\t\treturn false\n\t}\n\tl.mu.Lock()\n\tdefer l.mu.Unlock()\n\tl.advance(now)\n\tneed := float64(n)\n\tif l.tokens >= need {\n\t\tl.tokens -= need\n\t\treturn true\n\t}\n\treturn false\n}\n\n// Allow reports whether a single token may be consumed now.\nfunc (l *Limiter) Allow() bool {\n\treturn l.AllowN(l.clock.Now(), 1)\n}\n\n// Tokens returns the number of tokens currently available (approximate,\n// as of the current clock reading).\nfunc (l *Limiter) Tokens() float64 {\n\tl.mu.Lock()\n\tdefer l.mu.Unlock()\n\tl.advance(l.clock.Now())\n\treturn l.tokens\n}\n\nfunc min(a, b float64) float64 {\n\tif a < b {\n\t\treturn a\n\t}\n\treturn b\n}\n```\n\n```go\n// ratelimit_test.go\npackage ratelimit\n\nimport (\n\t\"testing\"\n\t\"time\"\n)\n\ntype fakeClock struct {\n\tt time.Time\n}\n\nfunc (f *fakeClock) Now() time.Time { return f.t }\nfunc (f *fakeClock) Advance(d time.Duration) {\n\tf.t = f.t.Add(d)\n}\n\nfunc mustNew(t *testing.T, rate float64, burst int, c Clock) *Limiter {\n\tt.Helper()\n\tl, err := NewLimiterWithClock(rate, burst, c)\n\tif err != nil {\n\t\tt.Fatalf(\"NewLimiterWithClock(%v, %d): %v\", rate, burst, err)\n\t}\n\treturn l\n}\n\nfunc TestConstructorValidation(t *testing.T) {\n\ttests := []struct {\n\t\tname string\n\t\trate float64\n\t\tburst int\n\t\tclock Clock\n\t\twantErr bool\n\t}{\n\t\t{\"zero rate\", 0, 1, realClock{}, true},\n\t\t{\"negative rate\", -1.5, 1, realClock{}, true},\n\t\t{\"zero burst\", 1, 0, realClock{}, true},\n\t\t{\"negative burst\", 1, -3, realClock{}, true},\n\t\t{\"nil clock\", 1, 1, nil, true},\n\t\t{\"valid\", 2.5, 5, realClock{}, false},\n\t}\n\tfor _, tc := range tests {\n\t\tt.Run(tc.name, func(t *testing.T) {\n\t\t\t_, err := NewLimiterWithClock(tc.rate, tc.burst, tc.clock)\n\t\t\tif (err != nil) != tc.wantErr {\n\t\t\t\tt.Fatalf(\"err = %v, wantErr = %v\", err, tc.wantErr)\n\t\t\t}\n\t\t})\n\t}\n}\n\nfunc TestAllowN(t *testing.T) {\n\tbase := time.Date(2024, 1, 1, 0, 0, 0, 0, time.UTC)\n\ttests := []struct {\n\t\tname string\n\t\trate float64\n\t\tburst int\n\t\tsteps []struct {\n\t\t\tadvance time.Duration\n\t\t\tn int\n\t\t\twant bool\n\t\t}\n\t}{\n\t\t{\n\t\t\tname: \"burst exhausted then denied\",\n\t\t\trate: 1, burst: 2,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{0, 2, true},\n\t\t\t\t{0, 1, false},\n\t\t\t},\n\t\t},\n\t\t{\n\t\t\tname: \"refill over time\",\n\t\t\trate: 1, burst: 2,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{0, 2, true},\n\t\t\t\t{0, 1, false},\n\t\t\t\t{time.Second, 1, true},\n\t\t\t\t{0, 1, false},\n\t\t\t\t{2 * time.Second, 2, true},\n\t\t\t\t{0, 1, false},\n\t\t\t},\n\t\t},\n\t\t{\n\t\t\tname: \"burst caps refill\",\n\t\t\trate: 10, burst: 2,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{0, 2, true},\n\t\t\t\t{10 * time.Second, 3, false}, // capped at 2\n\t\t\t\t{0, 2, true},\n\t\t\t},\n\t\t},\n\t\t{\n\t\t\tname: \"fractional refill\",\n\t\t\trate: 2, burst: 1,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{0, 1, true},\n\t\t\t\t{250 * time.Millisecond, 1, false}, // 0.5 tokens\n\t\t\t\t{250 * time.Millisecond, 1, true}, // 1.0 tokens\n\t\t\t},\n\t\t},\n\t\t{\n\t\t\tname: \"zero and negative n denied\",\n\t\t\trate: 1, burst: 1,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{0, 0, false},\n\t\t\t\t{0, -1, false},\n\t\t\t\t{0, 1, true},\n\t\t\t},\n\t\t},\n\t\t{\n\t\t\tname: \"request larger than burst never allowed\",\n\t\t\trate: 1, burst: 1,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{time.Hour, 2, false},\n\t\t\t},\n\t\t},\n\t}\n\n\tfor _, tc := range tests {\n\t\tt.Run(tc.name, func(t *testing.T) {\n\t\t\tfc := &fakeClock{t: base}\n\t\t\tl := mustNew(t, tc.rate, tc.burst, fc)\n\t\t\tfor i, s := range tc.steps {\n\t\t\t\tfc.Advance(s.advance)\n\t\t\t\tif got := l.AllowN(fc.Now(), s.n); got != s.want {\n\t\t\t\t\tt.Fatalf(\"step %d: AllowN(%v) = %v, want %v\", i, s.n, got, s.want)\n\t\t\t\t}\n\t\t\t}\n\t\t})\n\t}\n}\n\nfunc TestTokensRefill(t *testing.T) {\n\tfc := &fakeClock{t: time.Now()}\n\tl := mustNew(t, 1, 5, fc)\n\tl.AllowN(fc.t, 5)\n\tif got := l.Tokens(); got != 0 {\n\t\tt.Fatalf(\"tokens after drain = %v, want 0\", got)\n\t}\n\tfc.Advance(2 * time.Second)\n\tif got := l.Tokens(); got != 2 {\n\t\tt.Fatalf(\"tokens after 2s = %v, want 2\", got)\n\t}\n}\n\nfunc TestConcurrentAccess(t *testing.T) {\n\tfc := &fakeClock{t: time.Now()}\n\tl := mustNew(t, 1000, 1000, fc)\n\tdone := make(chan struct{})\n\tvar allowed int64\n\tvar mu sync.Mutex\n\tfor i := 0; i < 10; i++ {\n\t\tgo func() {\n\t\t\tdefer close(done)\n\t\t\tfor j := 0; j < 100; j++ {\n\t\t\t\tif l.AllowN(fc.Now(), 1) {\n\t\t\t\t\tmu.Lock()\n\t\t\t\t\tallowed++\n\t\t\t\t\tmu.Unlock()\n\t\t\t\t}\n\t\t\t}\n\t\t\tdone <- struct{}{}\n\t\t}()\n\t}\n\tfor i := 0; i < 10; i++ {\n\t\t<-done\n\t}\n\tif allowed > 1000 {\n\t\tt.Fatalf(\"allowed %d, exceeds burst 1000\", allowed)\n\t}\n}\n```\n\nNote: the test file needs `import \"sync\"` added to the import block (`sync` is used in `TestConcurrentAccess`).", + "output_characters": 6499, + "output_sha256": "7cee4b4018a96229ca3c68b566a2d229c881560c48627bb2d38cd96f287048da", + "prompt_tokens": 129, + "reasoning_characters": 27, + "reasoning_sha256": "0b3acc8403ec469b09b04be516f98e059799ba61dabe4c94250bbf24a6f23331", + "response_tail_seconds": 0.002874, + "run": 1, + "stream_done_marker": true, + "stream_sha256": "139f9d65b6f198abbd901e5c40312e9c1a1674283cb9024a5b3701e6074c04f7", + "time_to_first_visible_seconds": 0.958999, + "time_to_last_output_seconds": 32.397399, + "ttft_seconds": 0.72584, + "wall_seconds": 32.400273 + } + ], + "time_to_last_output_seconds": { + "maximum": 32.397, + "median": 32.397, + "minimum": 32.397, + "p90": 32.397 + }, + "ttft_seconds": { + "maximum": 0.726, + "median": 0.726, + "minimum": 0.726, + "p90": 0.726 + }, + "wall_seconds": { + "maximum": 32.4, + "median": 32.4, + "minimum": 32.4, + "p90": 32.4 + } + }, + "prose": { + "completion_gate": { + "passed": 1, + "total": 1 + }, + "decode_tokens_per_second": { + "maximum": 38.413, + "median": 38.413, + "minimum": 38.413, + "p90": 38.413 + }, + "runs": [ + { + "completion_tokens": 1002, + "completion_validation": { + "valid": true + }, + "decode_seconds": 26.059093, + "decode_tokens_per_second": 38.413, + "finish_reason": "stop", + "measured_sse_events": 1000, + "output": "**MEMO: GPU Capacity \u2014 Buy More or Use What We Have Better**\n\n**To:** Engineering Leadership\n**From:** Infrastructure Team\n**Re:** Hardware expansion decision for the inference cluster\n\n**Status and context**\n\nOur four A100 nodes are running hot. Peak-hour utilization sits around 85 percent, queue times for batch jobs have crept from minutes to hours, and interactive users are starting to notice latency in the notebook environment. The obvious question on the table is whether we should purchase two additional nodes, at roughly $180k all-in with networking and support, or invest a quarter of engineering time in squeezing more out of what we already own. I've spent the last two weeks looking at the data, and I want to lay out how I think we should reason about this before we commit either way.\n\n**What the numbers actually say**\n\nThe utilization figure is misleading on its own. Eighty-five percent sounds like we're nearly maxed out, but a breakdown by time of day tells a different story. Overnight and weekend utilization drops below 30 percent, and a meaningful share of our peak load comes from jobs that are not latency-sensitive at all \u2014 model evaluation runs, embedding backfills, fine-tuning experiments. These are jobs that could tolerate a delay of several hours without anyone noticing. Right now they compete with interactive work for the same capacity because our scheduler treats them identically. A modest investment in workload segregation, better preemption, and autoscaling our batch queue into the off-peak window would, by my estimate, free up 20 to 25 percent of peak capacity. That is the equivalent of roughly one additional node, purchased with engineer-weeks rather than capital.\n\nThere is also waste we can eliminate outright. Two teams are holding reservations on GPUs they use intermittently, and our current accounting makes it easier to squat on hardware than to release and re-request it. Fixing the incentive structure \u2014 showback rather than soft reservations, with a fast path for reclaiming idle capacity \u2014 costs almost nothing and recovers real headroom.\n\n**The case for buying anyway**\n\nThat said, I don't want to oversell optimization. Some of our peak demand is genuinely inelastic: the customer-facing inference service has a hard latency budget, and it degrades visibly when utilization crosses about 90 percent. If our growth forecast is even roughly right, optimization buys us perhaps two quarters of runway, not a permanent reprieve. There's also a procurement reality to face: lead times on these nodes are currently eight to ten weeks. If we wait until the pain is undeniable, we will spend a painful quarter queuing behind our own decision. Ordering hardware is slow to undo but also slow to arrive; the option has a long fuse.\n\n**Cost, risk, and reversibility**\n\nThe capital cost is real but not existential for us this year. The subtler cost is opportunity: $180k is also two engineers for a year, or the difference between hiring and not hiring on the product side. And hardware we buy is hardware we must power, cool, and eventually depreciate \u2014 a commitment that outlives the decision that created it. Optimization, by contrast, is largely reversible. If we restructure the scheduler and it turns out demand was higher than we thought, we've lost a few engineer-weeks and learned something precise about our true ceiling. If we buy nodes and demand turns out softer than forecast, we're carrying idle capital with no easy exit.\n\nReliability cuts both ways. More nodes means more redundancy and less exposure to a single failure taking down a class of jobs. But it also means more surface area to maintain, and our current operational load is already straining one part-time admin. Better utilization of a smaller fleet, with cleaner scheduling, is arguably the more reliable posture \u2014 provided we don't run so lean that a single node failure creates an outage rather than an inconvenience.\n\n**Recommendation**\n\nDo both, in sequence, with the order mattering. Start the optimization work now: scheduler improvements, reservation reform, and off-peak batch shifting. Give it six weeks and a hard success metric \u2014 peak utilization sustained below 75 percent with interactive latency within budget. Simultaneously, place a conditional purchase order for one node, not two, with delivery timed to land just after the optimization work concludes. If optimization delivers as expected, we cancel or defer the order and bank the savings. If it falls short, the hardware arrives before the situation becomes an incident.\n\nThe honest uncertainty here is demand itself. Our forecast has been wrong in both directions before, and the cheapest hedge is to keep our options open as long as possible. Sequencing a reversible intervention ahead of an irreversible purchase does exactly that. I'd like a decision by end of week so the purchase-order window doesn't slip.\n\nHappy to walk through the utilization data with anyone who wants the detail.", + "output_characters": 4993, + "output_sha256": "8541ed6e65bb98ccd9284e992c9f3ccec68163570b3db194b33ad18edd77c24e", + "prompt_tokens": 125, + "reasoning_characters": 67, + "reasoning_sha256": "0e329108922dfc1942b39bfd181fcdd024d698835cac088b9e4fca144d040576", + "response_tail_seconds": 0.002995, + "run": 1, + "stream_done_marker": true, + "stream_sha256": "c786ebb457fa5c963f00b27a4be89bc418929b31624cab0cb78fc5153056078d", + "time_to_first_visible_seconds": 1.21706, + "time_to_last_output_seconds": 26.775047, + "ttft_seconds": 0.715955, + "wall_seconds": 26.778042 + } + ], + "time_to_last_output_seconds": { + "maximum": 26.775, + "median": 26.775, + "minimum": 26.775, + "p90": 26.775 + }, + "ttft_seconds": { + "maximum": 0.716, + "median": 0.716, + "minimum": 0.716, + "p90": 0.716 + }, + "wall_seconds": { + "maximum": 26.778, + "median": 26.778, + "minimum": 26.778, + "p90": 26.778 + } + }, + "structured": { + "completion_gate": { + "passed": 1, + "total": 1 + }, + "decode_tokens_per_second": { + "maximum": 91.334, + "median": 91.334, + "minimum": 91.334, + "p90": 91.334 + }, + "runs": [ + { + "completion_tokens": 441, + "completion_validation": { + "valid": true + }, + "decode_seconds": 4.817508, + "decode_tokens_per_second": 91.334, + "finish_reason": "stop", + "measured_sse_events": 439, + "output": 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"0c3ac4015813af6a1b967659bd2289990fde895a5e43ae065b894d1e1f909ecb", + "prompts_version": "1.0.0", + "repository_dirty": true, + "repository_revision": "40fabcaf6e963ac27ab347f61d54a225dbd07419", + "repository_source": "git-worktree", + "repository_source_sha256": "71cf88ee30ff5e0c976ad5b1d8221af9115447a690039ac9aa020a85015fe300", + "repository_worktree_sha256": "7c17a097cc0dd9384713c172bf097b632f7898b6d6d33a978ce180de6a59e5cd", + "version": "1.1.0" + }, + "run": { + "appliance": { + "competing_traffic": "none", + "context_limit": 524288, + "drafter": "DFlash2 incoai/GLM-5.3-Flash-DFlash2, gamma 8, greedy", + "gpu_memory_utilisation": 0.91, + "hardware": "2 x NVIDIA DGX Spark, GB10, 128 GB unified memory per appliance", + "kv_cache_dtype": "fp8_g128", + "kv_cache_layout": "MLA latent fp8_g128 (8448 B per 16-token block per layer, 11 layers) + sparse-index cache (1024 B per block per layer + slot-mapped tails); 2 GiB of it in the GB10 display carveout", + "kv_cache_memory_per_rank": 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Group\n**Re:** How we should expand our AI compute capacity over the next two quarters\n\n**Summary and recommendation**\n\nWe are approaching the practical ceiling of our current local inference hardware. Peak-hour GPU utilisation regularly exceeds 85 percent, queue times during business hours have crept from seconds to minutes, and two recent incidents were caused or worsened by capacity contention rather than software faults. The instinctive response is to buy more machines. After looking at the numbers, my recommendation is that we spend the next quarter aggressively improving utilisation of what we already own, and defer the purchase decision by roughly ninety days. If utilisation work delivers what I expect, we will still need hardware eventually, but we will need less of it, buy it later at better prices, and understand our real demand profile far more precisely when we do.\n\n**The cost picture**\n\nThe capital case for new hardware is not as clean as it first appears. The quote we have for two additional accelerator nodes is roughly 40 percent of our annual infrastructure budget, and that figure excludes the hidden costs: rack power and cooling, which our facilities team estimates will add a recurring monthly sum; the engineering time to integrate, benchmark, and operate the new nodes; and the depreciation of equipment that historically loses resale value faster than our finance model assumes. Against that, the utilisation work is mostly labour we already pay for. Batch scheduling, model quantisation, better request coalescing, and moving batch-oriented jobs like embedding generation to overnight windows are all projects we have discussed for months and never prioritised because capacity pressure felt distant. It is no longer distant, but the cheapest capacity we can buy is the capacity we are currently wasting.\n\n**Latency and reliability**\n\nThe counterargument, and it is a serious one, is that utilisation optimisation does not add headroom, it only moves the ceiling. Our interactive workloads are latency-sensitive, and some of our recent queueing pain is structural: we run heterogeneous models on shared nodes, and a long batch job can add hundreds of milliseconds of tail latency to a user-facing request. Better scheduling and partitioning can mitigate this, but it is genuinely uncertain whether we can recover enough effective capacity through software alone. There is also a reliability dimension. Right now, a single node failure removes a third of our serving capacity. More hardware would improve redundancy as a side effect, which is a real benefit the pure cost comparison understates. If we defer the purchase, we are accepting another quarter of elevated single-point-of-failure risk, and I think we should be honest that this is a bet.\n\n**Opportunity cost**\n\nBoth options have opportunity costs, and they point in opposite directions. Buying hardware now consumes budget and attention that could go toward product work, and it locks us into an architecture chosen under time pressure. Improving utilisation consumes engineering hours that would otherwise go to features, and it risks becoming a distraction that delivers neither capacity nor product progress. The difference, in my view, is reversibility. Scheduling changes, quantisation, and workload placement are largely reversible and produce knowledge regardless of outcome. A hardware purchase is only partially reversible: we can resell machines, but at a loss, and the operating commitments around power and space are stickier still. When two options are close in expected value, the reversible one that teaches us more is usually the right first move.\n\n**What would change my mind**\n\nI want to be clear about the uncertainty here. If our utilisation audit shows that peak-hour demand is genuinely inelastic \u2014 that is, if interactive traffic alone accounts for sustained high utilisation with no batch work to displace \u2014 then software optimisation cannot save us, and we should place the hardware order immediately rather than burning a quarter pretending otherwise. I have asked for a two-week measurement exercise before we commit to the utilisation track, precisely so we are not optimising on intuition. Similarly, if the measurement shows our failure-domain risk has already materialised into customer-visible incidents, reliability may justify the purchase on its own.\n\n**Proposed next step**\n\nI propose we run the measurement audit over the next two weeks, then decide by a dated checkpoint whether to proceed with the utilisation quarter or trigger the purchase. Either way, we will have made the decision on evidence rather than anxiety, and that is worth the small delay.\n\nComments welcome before Friday.", + "output_characters": 4872, + "output_sha256": "15d166e4580c8659b59f7072413903932f521eac066fdb346eaf49ef90653e9a", + "prompt_tokens": 124, + "reasoning_characters": 62, + "reasoning_sha256": "7eddaec104351da3aba6059600be80b14d17920bd2df8ba3f1ac3d20ffb662d1", + "response_tail_seconds": 0.002802, + "stall": { + "arrival_window_gaps": 11, + "arrival_window_max_gap_seconds": 3.106082, + "arrival_window_p95_gap_seconds": 3.106082, + "events": 925, + "max_gap_seconds": 3.453105, + "median_gap_seconds": 0.045972, + "p95_gap_seconds": 0.066362, + "spanning_gap_seconds": 0.072945 + }, + "started_monotonic_seconds": 359838.477987, + "stream": 1, + "stream_done_marker": true, + "stream_sha256": "bee3b240c4f1a8afca5f452de487da764c4635c595c2c87dd48e0d38452027ad", + "time_to_first_visible_seconds": 1.088935, + "time_to_last_output_seconds": 39.186716, + "ttft_seconds": 0.71283, + "wall_seconds": 39.189517 + } + ], + "incumbents_finished_before_newcomer_first_output": 0, + "newcomer": { + "completion_tokens": 256, + "decode_seconds": 4.038134, + "decode_tokens_per_second": 63.148, + "event_seconds": [ + 12.608819, + 12.752734, + 12.752771, + 12.75278, + 12.752788, + 12.752799, + 12.752805, + 12.752811, + 12.752816, + 12.897151, + 12.897189, + 12.8972, + 12.897209, + 12.897217, + 12.897226, + 12.897234, + 12.897239, + 13.017799, + 13.017835, + 13.017844, + 13.017856, + 13.017861, + 13.017869, + 13.017875, + 13.017879, + 13.152663, + 13.152694, + 13.152702, + 13.152711, + 13.152718, + 13.152723, + 13.152751, + 13.152756, + 13.274949, + 13.274974, + 13.274982, + 13.27499, + 13.274996, + 13.275003, + 13.27501, + 13.275015, + 13.390532, + 13.390555, + 13.390564, + 13.390572, + 13.390588, + 13.390594, + 13.390599, + 13.511996, + 13.512031, + 13.512039, + 13.512048, + 13.512055, + 13.512066, + 13.512072, + 13.512078, + 13.639395, + 13.639432, + 13.639443, + 13.63945, + 13.639458, + 13.639464, + 13.639469, + 13.639475, + 13.759732, + 13.759769, + 13.759776, + 13.759783, + 13.759789, + 13.759794, + 13.759799, + 13.759804, + 13.877619, + 13.877648, + 13.877655, + 13.877662, + 13.877668, + 13.877677, + 13.877683, + 13.877687, + 14.001787, + 14.001824, + 14.00183, + 14.001837, + 14.001843, + 14.00185, + 14.001856, + 14.001862, + 14.114599, + 14.114628, + 14.114637, + 14.114644, + 14.114652, + 14.11466, + 14.114667, + 14.241052, + 14.241073, + 14.24108, + 14.241087, + 14.241094, + 14.2411, + 14.241105, + 14.241111, + 14.356957, + 14.356988, + 14.356995, + 14.357003, + 14.35701, + 14.357015, + 14.357026, + 14.357031, + 14.478747, + 14.478767, + 14.478774, + 14.478781, + 14.478788, + 14.478795, + 14.4788, + 14.478805, + 14.593098, + 14.593118, + 14.593126, + 14.593133, + 14.59314, + 14.593146, + 14.593151, + 14.593156, + 14.719473, + 14.719489, + 14.719497, + 14.719503, + 14.719509, + 14.719514, + 14.719519, + 14.719524, + 14.839298, + 14.839326, + 14.839332, + 14.839339, + 14.839345, + 14.839351, + 14.83936, + 14.839366, + 14.967143, + 14.967169, + 14.967177, + 14.967185, + 14.967191, + 14.967198, + 14.967205, + 14.967211, + 15.087564, + 15.087588, + 15.087594, + 15.087602, + 15.087609, + 15.087615, + 15.087621, + 15.087626, + 15.20594, + 15.205977, + 15.205983, + 15.205991, + 15.205998, + 15.206005, + 15.206011, + 15.206016, + 15.329825, + 15.329853, + 15.32986, + 15.329869, + 15.329876, + 15.329883, + 15.329895, + 15.329901, + 15.447462, + 15.447478, + 15.447485, + 15.447492, + 15.447499, + 15.447504, + 15.447511, + 15.447516, + 15.56541, + 15.565433, + 15.56544, + 15.565447, + 15.565453, + 15.565459, + 15.565464, + 15.565469, + 15.682157, + 15.682178, + 15.682185, + 15.682193, + 15.682198, + 15.682206, + 15.682211, + 15.682216, + 15.808512, + 15.808533, + 15.808541, + 15.808548, + 15.808555, + 15.808561, + 15.808566, + 15.808571, + 15.926033, + 15.926055, + 15.926062, + 15.92607, + 15.926077, + 15.926085, + 15.926091, + 15.926098, + 16.041535, + 16.041559, + 16.041566, + 16.041574, + 16.041592, + 16.041598, + 16.041603, + 16.041609, + 16.163743, + 16.163774, + 16.163782, + 16.16379, + 16.163797, + 16.163805, + 16.163811, + 16.163817, + 16.27953, + 16.279552, + 16.27956, + 16.279566, + 16.279573, + 16.279592, + 16.279599, + 16.279604, + 16.403304, + 16.403336, + 16.403345, + 16.403351, + 16.403358, + 16.403365, + 16.40337, + 16.403375, + 16.521754, + 16.521772, + 16.52178, + 16.521788, + 16.521795, + 16.521801, + 16.521807, + 16.521813, + 16.646954 + ], + "finish_reason": "length", + "finished_monotonic_seconds": 359856.847486, + "first_output_monotonic_seconds": 359852.802242, + "measured_sse_events": 256, + "output": " repository contains a service, tests, documentation, and release notes. 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Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. 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41.012622, + 41.012658, + 41.012673, + 41.064127, + 41.117506, + 41.117538, + 41.166972, + 41.220264, + 41.220314, + 41.269745, + 41.269776, + 41.320196, + 41.370075, + 41.420183, + 41.42021, + 41.469435 + ], + "finish_reason": "length", + "finished_monotonic_seconds": 359969.551092, + "first_output_monotonic_seconds": 359928.779057, + "measured_sse_events": 1023, + "output": "**MEMO**\n\n**To:** Platform Engineering Team\n**From:** Infrastructure Planning\n**Re:** GPU capacity \u2014 buy more, or use what we have better?\n**Date:** Internal discussion draft\n\nWe've reached the point where our inference fleet is running hot most weekdays, and the question in front of us is whether to purchase additional local hardware or to invest in squeezing more out of what we already own. I want to lay out how I'm thinking about this, because the right answer is less obvious than the queue depths suggest, and I'd like us to decide it deliberately rather than by default.\n\nThe case for buying more hardware is straightforward and emotionally compelling. Our p99 latency has crept from 400ms to nearly a second during peak hours, batch jobs are getting evicted more often, and every on-call engineer has stories about capacity contention causing an incident that shouldn't have happened. From a reliability standpoint, we're also thinner than I'd like: with our current fleet, losing a single node during peak traffic degrades service noticeably. A second node's worth of headroom would let us survive hardware failures, driver regressions, and maintenance windows without user-visible impact. Hardware is also an asset \u2014 it holds resale value reasonably well, and unlike cloud commitments, it's not a recurring bill.\n\nBut the case for better utilisation deserves a genuinely fair hearing, because our current numbers are not good. Across the fleet, our average GPU utilisation sits around 45%, and that figure flatters us; a lot of that is memory reserved by idle-but-allocated processes rather than useful compute. We have never implemented proper request batching across tenants, our model serving stack still cold-loads weights on several code paths, and our scheduling is first-come-first-served with no priority classes, which means batch work competes with latency-sensitive traffic on equal footing. Industry experience \u2014 and our own limited experiments \u2014 suggest that aggressive continuous batching plus a real scheduler could recover 30 to 50% of effective capacity. That is roughly the equivalent of one to two new nodes, at a fraction of the capital cost.\n\nThe cost comparison is where this gets interesting. A new node is roughly $90k up front plus power, cooling, and rack space, and it depreciates quickly in a market where model sizes and hardware requirements shift every eighteen months. The utilisation project, by contrast, is mostly engineering time: I estimate two engineers for six to eight weeks, with real risk of slippage. If we value that time honestly, it's not free \u2014 it's maybe $60\u201380k of opportunity cost, and those are engineers who would otherwise be working on product features. So the money is closer than it first appears, but the two options buy different things: hardware buys certainty, utilisation work buys capability. A better scheduler and batching layer make us permanently smarter about capacity, and that capability compounds as we grow. A node is just a node.\n\nReversibility matters here too. Buying hardware is a one-way door in practice \u2014 once it's racked and powered, the sunk cost anchors our architecture to it, and selling used accelerators recovers only part of the outlay. The utilisation project is almost entirely reversible: if continuous batching turns out to be a dead end for our workload mix, we've lost some weeks and learned something. That asymmetry argues for trying the cheap, reversible option before the expensive, sticky one.\n\nThere's also a timing argument. Our demand forecast is genuinely uncertain. If the new product surface launches in Q3 as hoped, we may need substantially more capacity by year end regardless of how well we optimise. If it slips or underperforms, we'll have bought hardware we don't need. Utilisation improvements are valuable in both scenarios; a new node is only clearly valuable in the first.\n\nMy recommendation, then: commit to the utilisation work now, with a hard scope and a decision checkpoint. Two engineers, eight weeks, targeting continuous batching, a priority-aware scheduler, and elimination of our worst cold-start paths. If we hit the utilisation targets and latency stays within SLO, we defer hardware purchase by at least two quarters. If we miss \u2014 either because the engineering is harder than expected or because demand grows faster than the recovered capacity \u2014 we buy the node, and we'll do so with much better information about how much headroom we actually need. I'd also propose a modest reliability hedge in the interim: a short-term cloud burst arrangement for failover capacity, which is expensive per hour but cheap as insurance and fully reversible.\n\nI want to be honest about the uncertainty in my own position. My utilisation estimates come partly from vendor benchmarks that may not survive contact with our workload, and engineering estimates of this kind are routinely optimistic by 50%. There's a real chance we spend six weeks and end up buying the hardware anyway, having spent time we could have spent shipping. 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The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. 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The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. 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The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. 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The obvious question on the table is whether we should purchase additional accelerators or invest in raising the utilisation of the fleet we already have. After several weeks of measurement and discussion, my recommendation is that we spend the next quarter improving utilisation first, and defer any purchase decision until we have data that justifies it. I want to walk through the reasoning, because the argument is less about money than it is about what we don't yet know.\n\nThe cost picture is the least interesting part of the debate, but it frames everything else. A new node would represent a capital outlay roughly equal to two full-time engineers for a year, and that figure understates the true expense. Accelerators depreciate quickly in a market where model architectures and memory requirements shift every few quarters, so the resale value of hardware bought today is likely to be disappointing by the time we might want", + "output_characters": 1240, + "output_sha256": "953a3e7bb1ff38a639eb3788197c482a8ad9b0dcfb299e810acdcb946e5230a1", + "prompt_tokens": 120, + "reasoning_characters": 62, + "reasoning_sha256": "7eddaec104351da3aba6059600be80b14d17920bd2df8ba3f1ac3d20ffb662d1", + "response_tail_seconds": 0.007783, + "started_monotonic_seconds": 359886.4642, + "stream": 1, + "stream_done_marker": true, + "stream_sha256": "42c44b2c1bc968b336eab2dad6feccd4f71bb9b47bc8dc9cbab664bcc9db278b", + "time_to_first_visible_seconds": 12.772162, + "time_to_last_output_seconds": 25.680445, + "ttft_seconds": 2.796839, + "wall_seconds": 25.688228 + } + ], + "newcomers_started_during_prefill": 1, + "overlap_valid": true, + "round": 1, + "short_solo_ttft_seconds": 0.706928, + "solo": { + "completion_tokens": 256, + "decode_seconds": 7.082919, + "decode_tokens_per_second": 36.002, + "event_seconds": [ + 0.706928, + 0.783932, + 0.833014, + 0.887786, + 0.887805, + 0.887813, + 0.887819, + 0.936062, + 0.936112, + 0.989107, + 0.989149, + 0.989157, + 0.989164, + 1.040999, + 1.041019, + 1.088836, + 1.088868, + 1.135007, + 1.184631, + 1.184676, + 1.238151, + 1.287531, + 1.287554, + 1.287561, + 1.336102, + 1.336127, + 1.385999, + 1.386027, + 1.446474, + 1.446516, + 1.446524, + 1.446531, + 1.446537, + 1.494917, + 1.494957, + 1.494965, + 1.544583, + 1.596504, + 1.596524, + 1.596531, + 1.644523, + 1.644565, + 1.694636, + 1.762978, + 1.763017, + 1.763025, + 1.81201, + 1.812051, + 1.861597, + 1.911363, + 1.957995, + 2.007319, + 2.062929, + 2.062953, + 2.06296, + 2.062966, + 2.113729, + 2.113749, + 2.113756, + 2.162282, + 2.211443, + 2.261381, + 2.312632, + 2.312655, + 2.362349, + 2.411567, + 2.459321, + 2.459352, + 2.507053, + 2.556921, + 2.6049, + 2.60492, + 2.604927, + 2.653334, + 2.700511, + 2.700546, + 2.750021, + 2.750068, + 2.750075, + 2.80115, + 2.801173, + 2.851839, + 2.900667, + 2.952155, + 3.00256, + 3.002585, + 3.05197, + 3.052007, + 3.102472, + 3.152035, + 3.201504, + 3.251101, + 3.300799, + 3.350552, + 3.405804, + 3.456048, + 3.456093, + 3.511015, + 3.511049, + 3.559176, + 3.608069, + 3.608089, + 3.608096, + 3.657342, + 3.708029, + 3.755495, + 3.755544, + 3.755551, + 3.806844, + 3.859106, + 3.859128, + 3.910539, + 3.910576, + 3.96004, + 4.008525, + 4.008551, + 4.056389, + 4.056434, + 4.056441, + 4.105224, + 4.105245, + 4.154587, + 4.202002, + 4.202031, + 4.252266, + 4.252291, + 4.302482, + 4.302528, + 4.302536, + 4.351997, + 4.40201, + 4.450278, + 4.450308, + 4.498468, + 4.548178, + 4.595495, + 4.642652, + 4.642685, + 4.689664, + 4.689717, + 4.738187, + 4.786617, + 4.786668, + 4.786676, + 4.83582, + 4.885054, + 4.933003, + 4.93303, + 4.979976, + 5.029052, + 5.076924, + 5.125862, + 5.172496, + 5.222123, + 5.222145, + 5.222152, + 5.270431, + 5.270462, + 5.319176, + 5.367483, + 5.367511, + 5.418551, + 5.418578, + 5.470152, + 5.522476, + 5.522497, + 5.522504, + 5.572057, + 5.620812, + 5.677514, + 5.677561, + 5.67757, + 5.677576, + 5.725849, + 5.725872, + 5.773321, + 5.773365, + 5.773373, + 5.820348, + 5.820373, + 5.866282, + 5.915886, + 5.971198, + 6.019799, + 6.019825, + 6.067815, + 6.067843, + 6.114857, + 6.114881, + 6.16339, + 6.163411, + 6.163418, + 6.213664, + 6.213711, + 6.213718, + 6.262436, + 6.312542, + 6.312567, + 6.363183, + 6.414502, + 6.465767, + 6.465796, + 6.522071, + 6.522109, + 6.522118, + 6.522125, + 6.577916, + 6.577941, + 6.62711, + 6.627131, + 6.676506, + 6.676535, + 6.725537, + 6.725583, + 6.72559, + 6.775759, + 6.825053, + 6.825086, + 6.875619, + 6.925848, + 6.925868, + 6.925875, + 6.973982, + 6.97401, + 7.022384, + 7.022404, + 7.022411, + 7.070698, + 7.118346, + 7.118397, + 7.118404, + 7.173129, + 7.173153, + 7.225233, + 7.225275, + 7.225284, + 7.225291, + 7.27315, + 7.327783, + 7.376624, + 7.376655, + 7.376662, + 7.424155, + 7.4745, + 7.551344, + 7.551364, + 7.551371, + 7.551377, + 7.599806, + 7.646474, + 7.646512, + 7.695199, + 7.742147, + 7.789823, + 7.789847 + ], + "finish_reason": "length", + "finished_monotonic_seconds": 359885.462089, + "first_output_monotonic_seconds": 359878.376478, + "measured_sse_events": 255, + "output": "**MEMO: GPU Capacity \u2014 Buy More or Use What We Have Better?**\n\n**To:** Engineering Leadership\n**From:** Infrastructure Team\n**Re:** Recommendation on AI hardware capacity for the next two quarters\n\n**Status and problem**\n\nOur four A100 nodes currently run at roughly 55% average utilization, but that number hides the real problem: peak demand on weekday mornings and during model-evaluation windows pushes us above 90%, while nights and weekends sit near 20%. The consequence is queuing. Data scientists wait hours for training jobs to start, and our internal inference endpoint occasionally breaches its 800ms p95 latency target. Two teams have asked whether they should rent cloud capacity instead, which would fragment our workflow and raise spend. The question on the table is whether to purchase two additional nodes (~$180k installed) or invest in scheduling, batching, and caching improvements to raise effective utilization on the fleet we already own.\n\n**The cost picture**\n\nThe purchase case is straightforward but weaker than it first appears. Beyond the capital cost, each added node carries power, cooling, and maintenance overhead we estimate at $2,100/month, and hardware depreciates quickly in a market where", + "output_characters": 1225, + "output_sha256": "f3804bdef3723dcb930369b65ca7a1ce1249991e088e176ab7a0d4d40215568c", + "prompt_tokens": 122, + "reasoning_characters": 62, + "reasoning_sha256": "7eddaec104351da3aba6059600be80b14d17920bd2df8ba3f1ac3d20ffb662d1", + "response_tail_seconds": 0.002692, + "started_monotonic_seconds": 359877.669551, + "stream_done_marker": true, + "stream_sha256": "ef0dda582f2accb14a2249fa34dd7d2bc2559735ccdad242b9c2768656eca255", + "time_to_first_visible_seconds": 1.088836, + "time_to_last_output_seconds": 7.789847, + "ttft_seconds": 0.706928, + "wall_seconds": 7.792539 + } + }, + { + "incumbent": { + "completion_tokens": 1024, + "decode_seconds": 14.731666, + "decode_tokens_per_second": 69.442, + "event_seconds": [ + 13.289194, + 13.412007, + 13.412072, + 13.412089, + 13.412103, + 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Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. 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At the same time, two teams have told us they're blocked on inference capacity, and finance has flagged the hardware line item as a candidate for expansion. The instinct in the room is that we're capacity-constrained and should buy our way out. I want to argue, carefully, that we are not capacity-constrained \u2014 we are utilization-constrained \u2014 and that the two problems have very different price tags, risk profiles, and consequences.\n\nStart with cost. A realistic quote for the hardware we'd want is in the low six figures once you include networking, rack power, cooling adjustments, and the engineering time to integrate and operate it. That's not an impossible number for us, but it's a number that commits us for three to five years, because GPUs depreciate slowly", + "output_characters": 1185, + "output_sha256": "d17f304e7c0080598df28ffc2680dbdf5c8c0dcc194b799775af78892ad139df", + "prompt_tokens": 124, + "reasoning_characters": 67, + "reasoning_sha256": "534bd8205f1c9b08ab4e9db6347d5c54d56a7224c969af4cb9f5ee2b9e7b8bed", + "response_tail_seconds": 0.00748, + "started_monotonic_seconds": 359977.935296, + "stream": 1, + "stream_done_marker": true, + "stream_sha256": "4d07fed51ae9baa3877a78db8473502962aaf827093e05b492707b641e286ffe", + "time_to_first_visible_seconds": 12.776385, + "time_to_last_output_seconds": 25.922221, + "ttft_seconds": 2.806985, + "wall_seconds": 25.929701 + } + ], + "newcomers_started_during_prefill": 1, + "overlap_valid": true, + "round": 2, + "short_solo_ttft_seconds": 0.711601, + "solo": { + "completion_tokens": 256, + "decode_seconds": 6.66545, + "decode_tokens_per_second": 38.257, + "event_seconds": [ + 0.711601, + 0.78563, + 0.83453, + 0.881139, + 0.881171, + 0.929864, + 0.929881, + 0.929889, + 0.979488, + 1.034129, + 1.034164, + 1.034172, + 1.034177, + 1.08514, + 1.085157, + 1.085164, + 1.134802, + 1.134819, + 1.134833, + 1.184787, + 1.184822, + 1.184838, + 1.236142, + 1.236164, + 1.236171, + 1.285716, + 1.285736, + 1.330553, + 1.380656, + 1.380689, + 1.428326, + 1.477393, + 1.47744, + 1.525558, + 1.525608, + 1.574888, + 1.625114, + 1.625134, + 1.62514, + 1.677515, + 1.677541, + 1.729921, + 1.729975, + 1.729984, + 1.729991, + 1.78009, + 1.842181, + 1.842201, + 1.842209, + 1.842215, + 1.842221, + 1.892638, + 1.892658, + 1.892666, + 1.942535, + 1.942555, + 1.942562, + 1.991909, + 2.041909, + 2.041943, + 2.091128, + 2.142394, + 2.195045, + 2.195091, + 2.243026, + 2.29221, + 2.340509, + 2.389895, + 2.440177, + 2.488516, + 2.488536, + 2.488543, + 2.538771, + 2.538856, + 2.591416, + 2.591473, + 2.591488, + 2.641573, + 2.641627, + 2.641642, + 2.688903, + 2.737974, + 2.738017, + 2.784049, + 2.833139, + 2.833185, + 2.8332, + 2.881561, + 2.881614, + 2.93065, + 2.930695, + 2.979114, + 2.979161, + 2.979181, + 3.030781, + 3.030857, + 3.081682, + 3.081725, + 3.13035, + 3.13039, + 3.180593, + 3.228717, + 3.277283, + 3.326602, + 3.326643, + 3.37719, + 3.377236, + 3.427052, + 3.427094, + 3.476798, + 3.524848, + 3.576511, + 3.576547, + 3.626484, + 3.677724, + 3.67778, + 3.677795, + 3.728414, + 3.777019, + 3.826983, + 3.827022, + 3.874687, + 3.924434, + 3.924464, + 3.974806, + 3.974848, + 4.023359, + 4.070663, + 4.070711, + 4.119818, + 4.119896, + 4.119912, + 4.170931, + 4.170962, + 4.220028, + 4.26865, + 4.319962, + 4.320004, + 4.320019, + 4.372115, + 4.372161, + 4.372176, + 4.421152, + 4.421193, + 4.471119, + 4.471158, + 4.519057, + 4.519106, + 4.569308, + 4.631363, + 4.631407, + 4.681538, + 4.681585, + 4.730108, + 4.730152, + 4.779205, + 4.779248, + 4.779262, + 4.826184, + 4.875139, + 4.924749, + 4.924795, + 4.976507, + 4.976549, + 4.976564, + 5.02595, + 5.025994, + 5.02601, + 5.075915, + 5.075967, + 5.155526, + 5.202839, + 5.202884, + 5.253134, + 5.253181, + 5.303428, + 5.30347, + 5.354584, + 5.403413, + 5.450884, + 5.450925, + 5.49925, + 5.499291, + 5.499306, + 5.546191, + 5.546231, + 5.595057, + 5.595103, + 5.647642, + 5.700388, + 5.700437, + 5.700451, + 5.749845, + 5.749888, + 5.749903, + 5.800395, + 5.850241, + 5.850281, + 5.900786, + 5.900858, + 5.900874, + 5.948505, + 5.948546, + 5.997998, + 6.049213, + 6.049262, + 6.098883, + 6.147686, + 6.197418, + 6.197473, + 6.197488, + 6.245866, + 6.245901, + 6.296079, + 6.345339, + 6.34538, + 6.393982, + 6.394019, + 6.394034, + 6.442588, + 6.503515, + 6.50356, + 6.503576, + 6.503589, + 6.551011, + 6.597063, + 6.643376, + 6.643418, + 6.643433, + 6.690947, + 6.741125, + 6.741164, + 6.741179, + 6.788142, + 6.788181, + 6.836761, + 6.885433, + 6.885471, + 6.933745, + 6.983244, + 7.032524, + 7.08803, + 7.088074, + 7.160514, + 7.160546, + 7.160562, + 7.160575, + 7.160587, + 7.160597, + 7.209489, + 7.266239, + 7.26628, + 7.325517, + 7.325557, + 7.377051 + ], + "finish_reason": "length", + "finished_monotonic_seconds": 359976.93305, + "first_output_monotonic_seconds": 359970.264777, + "measured_sse_events": 255, + "output": "**MEMO: GPU Capacity \u2014 Buy More or Use What We Have Better**\n\n**To:** Engineering Leadership\n**From:** Infrastructure Team\n**Re:** Recommendation on our next capacity decision\n\n**Status: Recommendation to pursue utilisation improvements before hardware purchase**\n\nOver the past quarter, our local inference hardware has become the constraint on nearly everything we want to ship. Queue times for the shared cluster have crept upward, batch jobs are being deferred to weekends, and two planned experiments were shelved outright because neither could get guaranteed GPU time. The obvious question on the table is whether we buy more hardware or extract more from what we own. After looking at the numbers and talking through the operational realities, my recommendation is that we invest the next quarter in utilisation improvements, with a purchase decision deferred until we see what that work yields.\n\nThe cost argument alone does not settle this, but it frames it. A meaningful hardware expansion \u2014 enough to actually relieve the pressure rather than merely postpone it \u2014 would consume the majority of our annual capital budget. That is money we cannot spend twice. 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63.61721, + 63.713231, + 63.811855, + 63.811882, + 63.923028, + 64.019154, + 64.019208, + 64.019222, + 64.113661, + 64.113722, + 64.113737, + 64.195181, + 64.195243, + 64.292129, + 64.292158, + 64.408559, + 64.515138 + ], + "finish_reason": "stop", + "finished_monotonic_seconds": 360084.244795, + "first_output_monotonic_seconds": 360020.434316, + "measured_sse_events": 957, + "output": "**MEMO: GPU Capacity \u2014 Buy More or Use What We Have Better**\n\n**To:** Engineering Leadership\n**From:** Infrastructure Team\n**Re:** Inference capacity planning for the next two quarters\n\nWe've been asked to decide between two paths: purchasing additional local GPU hardware to meet growing inference demand, or investing engineering effort to raise utilisation of the fleet we already own. After several weeks of measurement and modelling, our recommendation is that we should pursue utilisation improvements first, with a small, carefully scoped hardware purchase as a hedge \u2014 and we want to walk through the reasoning honestly, including where we could be wrong.\n\nStart with the economics. Our current fleet runs at roughly 38% average utilisation, with a diurnal pattern that peaks near 70% mid-afternoon and falls below 15% overnight. That means we own roughly twice the compute we actually use in an average hour. The capital cost of doubling capacity \u2014 which is effectively what buying our way out of the current contention would do \u2014 is on the order of $180K upfront, plus incremental power, cooling, and rack space that push the true annual cost closer to $60K. By contrast, the utilisation work we propose is roughly two engineers for six to eight weeks: batch-aware request routing, a proper priority queue so interactive traffic can preempt batch jobs, and dynamic partitioning of the two large cards that are currently pinned to single tenants. The engineering cost is real, but it is an order of magnitude smaller, and unlike hardware, it compounds \u2014 every scheduling improvement we build keeps paying off as we grow.\n\nLatency is the strongest argument on the other side, and we take it seriously. Our p95 inference latency has degraded from 240ms to 410ms over the past quarter, and that regression is visible in user-facing metrics. Squeezing more work through existing hardware risks making this worse before it gets better; queueing discipline and batching trade throughput against tail latency, and if we get the knobs wrong, we can hurt the product while congratulating ourselves on efficiency. This is why our proposal sequences the work: we would first implement the priority queue and preemption, which should *improve* interactive latency immediately by protecting it from batch load, and only then tighten batching thresholds while watching p99 closely. If tail latency degrades beyond agreed thresholds at any point, we stop and revisit.\n\nReliability cuts both ways. More hardware means more redundancy \u2014 a single failed node today takes out a meaningful fraction of capacity, and a second node would halve that blast radius. But it also means more things to fail, more firmware to patch, and a larger maintenance surface for a team that is already stretched thin. Improving utilisation, by contrast, lets us consolidate workloads and take marginal nodes out of rotation, which actually improves our maintenance posture: we can drain and service machines without contention because we're no longer running everything at the edge of capacity. The reliability question is genuinely close, and we'd welcome input from SRE before finalising.\n\nThe opportunity cost deserves explicit mention. The two engineers we'd assign to utilisation work are the same people we'd want on the model-quality roadmap next quarter. Hardware purchase doesn't consume their time \u2014 but it does consume budget that could fund a third hire, and it commits us to a rack footprint and power contract that constrain next year's options. Which brings us to reversibility, the factor we weight most heavily. Hardware is nearly irreversible: cards depreciate, power contracts lock in, and if inference demand shifts to a different model size or architecture \u2014 a real possibility given how quickly our roadmap has moved this year \u2014 the fleet we buy today may be poorly matched to the workloads of eighteen months from now. Scheduling software, by contrast, is almost fully reversible and portable across whatever hardware we eventually run.\n\nSo the recommendation: fund the utilisation project now, with a checkpoint at week six. If utilisation has moved above 55% and p95 latency is back under 300ms, we defer hardware purchase entirely and re-evaluate in Q3. If the work stalls, or demand growth outpaces the gains, we execute a pre-negotiated purchase order for two nodes \u2014 sized for the *smallest* configuration that relieves contention, not the one that maximises headroom. That keeps the option open without exercising it prematurely.\n\nWe are confident in the direction and appropriately humble about the numbers. Utilisation projections are notoriously optimistic, and a single large customer onboarding could change the calculus overnight. We'll revisit this memo's assumptions monthly.\n\nHappy to walk through the data in Thursday's infra review.", + "output_characters": 4844, + "output_sha256": "735df9fb97f2223d6356516fce0be0b61eb75eb58323cbeaec48a39294257ee4", + "prompt_tokens": 122, + "reasoning_characters": 62, + "reasoning_sha256": "7eddaec104351da3aba6059600be80b14d17920bd2df8ba3f1ac3d20ffb662d1", + "response_tail_seconds": 0.010439, + "stall": { + "arrival_window_gaps": 10, + "arrival_window_max_gap_seconds": 3.153031, + "arrival_window_p95_gap_seconds": 3.153031, + "events": 957, + "max_gap_seconds": 3.613826, + "median_gap_seconds": 0.075089, + "p95_gap_seconds": 0.124197, + "spanning_gap_seconds": 0.093229 + }, + "started_monotonic_seconds": 360019.719219, + "stream": 1, + "stream_done_marker": true, + "stream_sha256": "a0cbe3efbdfcf82f9e9a25eded40bc6e266f7a081d1b2b41d51ea753560affc2", + "time_to_first_visible_seconds": 2.607854, + "time_to_last_output_seconds": 64.515138, + "ttft_seconds": 0.715097, + 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The trigger is familiar. Our inference queues are backing up during peak hours, batch jobs are being pushed to evenings, and a couple of teams have started talking about latency as though it were an unavoidable tax. The instinctive response is to buy more cards. Having looked at the numbers, I think that instinct is half right, and the half that's wrong is expensive.\n\nStart with utilisation, because it's the number we control most directly. Across our current fleet, measured GPU utilisation sits around 35\u201340% on a weekly average, with sharp peaks in the mid-morning and a long trough overnight. That means we own roughly twice the compute we actually use. The problem is not capacity in aggregate; it is capacity at the wrong times, allocated to the wrong workloads, and fragmented across teams that provision but rarely release. Some of this is scheduling: our batch inference jobs could run in the overnight trough with almost no user-visible impact. Some of it is fragmentation: several single-GPU boxes are pinned by long-running experiments that are checked on weekly, holding memory hostage while doing nothing. And some of it is simply that nobody owns the queue. We have no internal pricing signal, no reclamation policy, and no shared scheduler, so every team rationally hoards.\n\nAgainst that backdrop, the case for buying more hardware looks weaker than it did. A modest expansion \u2014 say two to four high-end cards \u2014 would cost somewhere in the low six figures once you include power, cooling, and the rack capacity we'd need to find. That is not a catastrophic sum for us, but it is real money that would otherwise fund headcount, tooling, or a vendor API budget that would let us burst to the cloud during peaks. The opportunity cost matters more than the sticker price. Every dollar spent on metal that idles 60% of the time is a dollar not spent on the product work that actually differentiates us.\n\nLatency is the strongest argument for buying, and I want to take it seriously. Our user-facing inference p95 degrades noticeably during the morning peak, and no amount of clever scheduling fully fixes a queue that is genuinely saturated for two hours a day. But the fix for peak-hour saturation does not have to be owned capacity. A hybrid arrangement \u2014 local hardware for baseline load, cloud API for peak spillover \u2014 would cost a few thousand dollars a month at our current volumes and would cut p95 immediately, while we fix the underlying scheduling. If peak demand grows and stays high, that growth itself becomes the evidence justifying a purchase, made with better data than we have today.\n\nReliability cuts both ways. More hardware means more redundancy, which is genuinely valuable \u2014 right now a single card failure can take a production service offline. But it also means more failure surface, more maintenance, and more of our senior engineers' time spent on rack-and-stack work rather than product. Improving utilisation, by contrast, requires software and process: a shared scheduler, better queueing, autoscaling between local and cloud, and honest instrumentation. That work is durable engineering value regardless of what we eventually buy.\n\nWhich brings me to reversibility, the quiet tiebreaker. Hardware is a one-way door. Cards depreciate, power contracts lock in, and rack space is finite; if demand shifts to a different model architecture or a vendor offering improves, we cannot easily unwind the purchase. Utilisation improvements are almost entirely reversible. If we build the scheduler and the hybrid burst path and it turns out we were wrong about our own demand, we have lost little and learned a great deal. If we buy and we were wrong, we have sunk capital and a maintenance burden.\n\nSo my recommendation is explicit: do not buy hardware this quarter. Fund a focused utilisation effort \u2014 shared scheduling, overnight batch migration, reclamation of idle allocations, and a cloud burst arrangement for peaks \u2014 with a hard review in ninety days. If utilisation rises above 70% and p95 still misses target, the purchase case will make itself, and we will make it with confidence.\n\nI want to be honest about the uncertainty here. Our demand forecasts are shaky, model sizes are growing in ways that could outpace any scheduling cleverness, and there is a real risk that the utilisation project becomes a distraction if it drifts. We should scope it tightly and staff it with people who want to do it. 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The case is not that we will never need more GPUs \u2014 it is that we do not yet know enough to buy them well, and the cheapest, most reversible way to buy that knowledge is to fix what we already own.\n\n**Where we stand**\n\nOur eight A100s currently run at roughly 34 percent average utilisation, with a diurnal swing from under 15 percent overnight to peaks near 80 percent during business hours. That peak number is what people remember, and it is what fuels the instinct to buy. But peaks are the wrong lens. Capacity planning against peak means paying for hardware that idles most of the day, and right now we are doing exactly that with machines we already own. If we cannot get our current fleet above 60 percent sustained, buying more of the same will not solve the underlying problem; it will simply double the amount of idle capacity we are amortising.\n\n**Cost**\n\nThe purchase option is expensive in more ways than the invoice. A two-node expansion would cost approximately $380K upfront, plus roughly $4K monthly in power, cooling, and colocation. More importantly, it is a bet on our current workload shape staying constant. GPU generations turn over every 18 to 24 months, and hardware bought today at peak pricing will be worth a fraction of that on the secondary market when our needs change. Depreciation on underused accelerators is the quietest and largest waste in most ML budgets, and we would be adding to it.\n\nBy contrast, the utilisation work \u2014 batch scheduling, request coalescing, quantisation of our two largest models, and a proper queueing layer \u2014 is mostly engineering time we would spend anyway. Even attributing two engineers for a quarter, the cost is a fraction of the hardware option, and it produces assets (scheduling infrastructure, telemetry) that retain value regardless of what we eventually buy.\n\n**Latency**\n\nThe honest counterargument is latency. Our interactive workloads have a p95 budget of 400ms, and coalescing requests or batching more aggressively trades tail latency for throughput. This is a real risk, but it is a managed one: modern continuous batching can raise throughput substantially while keeping tail latency within budget, and we can enforce per-tenant SLOs at the queue layer. If utilisation work pushes p95 past acceptable bounds, we will know quickly and can dial back \u2014 a feedback loop measured in days, not procurement cycles.\n\n**Reliability**\n\nThere is also a reliability argument that cuts both ways. More hardware means more redundancy against node failure, and our current single-node failure does degrade service for the affected model. But redundancy bought through utilisation improvements \u2014 better failover, faster checkpoint recovery, workload migration \u2014 addresses the same risk more cheaply. And a larger fleet with poor scheduling is arguably less reliable in practice, because capacity is harder to reason about when it is spread thin across more machines with low headroom everywhere.\n\n**Opportunity cost**\n\nThe deeper question is what else the money could do. $380K is roughly a year of a senior engineer, or the entire budget for our evaluation and safety tooling, which is currently underfunded. Buying hardware that sits idle 60 percent of the time is a decision to fund silicon instead of people, and given that our bottleneck over the next two quarters is more likely to be model quality and evaluation than raw throughput, that trade looks wrong to me.\n\n**Reversibility**\n\nFinally, reversibility. Hardware purchases are nearly irreversible at our scale \u2014 resale value is poor, and the commitment shapes our architecture for years. Utilisation work is the opposite: every piece of it (scheduling, batching, quantisation) is additive and reversible, and it makes a future purchase *more* effective, because whatever we buy later will arrive into a fleet that knows how to use it. Buying hardware now, before the utilisation work, would mean paying full price to defer a problem we could have solved cheaply.\n\n**Uncertainty**\n\nI want to be clear about what we do not know. Our traffic forecast has wide error bars; if the new customer pipeline closes, demand could double within two quarters, and utilisation work alone would not cover that. There is also lead-time risk: if we decide in Q3 that we do need hardware, a 10\u201314 week procurement delay could hurt. To hedge, I propose we begin vendor qualification now \u2014 pricing, lead times, and a small evaluation unit if one is available \u2014 so that a purchase decision in Q3 is fast rather than cold-started. 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Leadership\n**From:** Infrastructure Team\n**Re:** Recommendation on AI hardware capacity for the next two quarters\n\nWe've been asked to decide whether to purchase additional local GPU hardware or to invest in improving utilisation of the fleet we already own. After several weeks of measurement, modelling, and argument, we have a recommendation, though it comes with honest caveats about what we still don't know.\n\n**The current situation**\n\nOur four A100-class nodes are, on paper, capable of handling our current workload mix \u2014 inference for the internal assistant, batch embedding jobs, and the training runs our research group schedules a few times a month. In practice, average utilisation sits around 38 percent, but that number is misleading. Peak demand on weekday afternoons pushes two of the four nodes to saturation, and the research team's training jobs, which can't be preempted without losing days of work, block inference capacity for hours at a time. The result is that our p95 inference latency has crept from 400ms to over 2 seconds during peak windows, which is now visibly degrading the assistant experience and generating complaints from the support team.\n\nThe obvious fix is to buy two more nodes. At current pricing that's roughly $180k in capital plus perhaps $15k annually in power, cooling, and maintenance. That's not a catastrophic sum for us, but it's not trivial either, and it would consume most of our remaining hardware budget for the year.\n\n**Why we don't think buying is the right first move**\n\nThe strongest argument against purchasing is that we would be buying capacity to serve a scheduling problem, not a capacity problem. Our analysis suggests that if we implemented proper workload partitioning \u2014 reserving two nodes for latency-sensitive inference, one for batch, and one as a flexible pool \u2014 we could hold p95 latency under 700ms at current demand without any new hardware. The training jobs would run overnight or on the flexible node with queueing, which the research team has informally agreed to tolerate if it means the assistant stops timing out.\n\nThere's also the opportunity cost to weigh. That $180k spent on GPUs is $180k not spent on the observability overhaul we've deferred twice, or on the storage expansion that our dataset team has been requesting since spring. Hardware depreciates quickly in this market; the A100s we'd buy today will be worth a fraction of that in three years, and the rental market for on-demand capacity has never been cheaper if we hit a genuine crunch.\n\nReversibility matters too. Buying hardware is a one-way door in practical terms \u2014 once purchased, the money is gone, and the sunk-cost pressure to keep the machines busy will distort future decisions. Improving utilisation, by contrast, is almost entirely reversible. If our scheduling changes prove too disruptive, we can revert in an afternoon. If they succeed, we'll have bought ourselves time and, more importantly, information: a much better picture of our true demand curve before we commit capital against it.\n\n**What we propose**\n\nWe recommend a three-month programme of utilisation work: workload partitioning, a proper job queue with priorities, autoscaling of batch work into off-peak windows, and better per-tenant metering so we can see who is actually consuming what. The engineering cost is roughly six weeks of one engineer's time, which is real but modest. We'd set explicit success criteria: p95 inference latency under 700ms sustained, training job turnaround under 24 hours, and utilisation above 55 percent. If we hit those targets and demand keeps growing, the case for a purchase in the next budget cycle becomes strong and well-evidenced rather than reactive.\n\n**Honest uncertainty**\n\nWe should be candid about the risks. Our demand forecast is based on twelve months of history, and if the assistant's usage doubles after the planned product launch, utilisation work alone won't save us \u2014 we'd be buying hardware under time pressure, which is when organisations make their worst procurement decisions. There's also a real chance the scheduling work is harder than we estimate; preemption and partitioning on shared GPU infrastructure has burned better teams than ours. And the research group's patience with queueing is an assumption, not a commitment.\n\nOn balance, though, the asymmetry favours waiting. The cost of six weeks of engineering time is small; the cost of $180k spent on hardware we may not need, or of buying in a panic later, is large. We recommend proceeding with the utilisation programme immediately and revisiting the purchase decision with real data in Q3.\n\nHappy to walk through the utilisation data in detail at Thursday's review.", + "output_characters": 4798, + "output_sha256": "35fa6f91dd392e7bf4b798ea7dba2ca8c36d2c8f57184344c7f9e89fb25e178c", + "prompt_tokens": 120, + "reasoning_characters": 62, + "reasoning_sha256": "7eddaec104351da3aba6059600be80b14d17920bd2df8ba3f1ac3d20ffb662d1", + "response_tail_seconds": 0.007455, + "stall": { + "arrival_window_gaps": 13, + "arrival_window_max_gap_seconds": 3.139274, + "arrival_window_p95_gap_seconds": 3.127476, + "events": 984, + "max_gap_seconds": 3.472983, + "median_gap_seconds": 0.069623, + "p95_gap_seconds": 0.119296, + "spanning_gap_seconds": 0.106311 + }, + "started_monotonic_seconds": 360145.985292, + "stream": 1, + "stream_done_marker": true, + "stream_sha256": "d0983a9fe2f05e1d6b6f47cdc305cf2df934f943f72a1fcc7746449fe28ba638", + 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two quarters\n\n**Summary and recommendation**\n\nWe recommend that we spend the next six to eight weeks improving utilisation of our existing GPU fleet before committing to any new hardware purchases. If utilisation improvements stall below roughly 55 percent sustained, or if product demand accelerates faster than our current forecast, we should revisit the purchase decision with better data in hand. This is not a deferral for its own sake; it is the cheapest way to buy information, and the option value of waiting is currently higher than the cost of occasional queuing.\n\n**The situation**\n\nOur four A100 nodes currently run at an average of 38 percent utilisation, but that average hides a problematic pattern. Utilisation peaks above 85 percent during business hours in our primary region and drops below 15 percent overnight and on weekends. Meanwhile, our latency SLO is breached roughly twice a week during peak windows, and the obvious fix \u2014 buying two more nodes \u2014 has been proposed. The capital cost is significant: approximately $180K upfront, plus roughly $2K per month per node in power, cooling, and colocation, and a realistic three-year depreciation horizon given how quickly inference-optimised hardware is improving.\n\n**Why buying now is the wrong first move**\n\nThe case for buying rests on a real problem, but it treats our worst hour as if it were our average hour. We are not capacity-constrained in any meaningful sense for most of the week; we are peak-constrained for a few hours a day. Buying hardware to solve a peak problem means paying for idle metal the rest of the time. Before we spend $180K on capacity that sits dormant 70 percent of the hours in a week, we should exhaust the much cheaper levers.\n\nThose levers are well understood. Batch size tuning alone has historically yielded 20 to 30 percent throughput gains in systems like ours, at the cost of a modest latency increase that our SLOs can absorb. Better request routing, quantisation of our two largest models, aggressive caching of common completions, and time-shifting our internal batch evaluation jobs to off-peak windows would together plausibly lift effective capacity by 40 to 60 percent. None of this is speculative engineering; it is mostly configuration, measurement, and discipline. The engineering cost is perhaps three to four engineer-weeks \u2014 a rounding error against the capital purchase.\n\n**Latency and reliability considerations**\n\nI want to be honest about the trade-off here. Some utilisation improvements, particularly larger batch sizes, trade throughput for tail latency. Our p99 currently sits at 1.9 seconds; pushing batches harder could push it toward 2.4 seconds unless we implement admission control. That is why the work must include a proper load-shedding and priority-queueing layer, not just tuning knobs. Reliability is the other argument people reach for when advocating more hardware: more nodes means more redundancy. That is true, but our recent incidents were caused by a misconfigured load balancer and a stuck batch job, not by capacity exhaustion. Redundancy purchased to mask operational fragility tends to hide problems rather than fix them. The reliability work \u2014 better canaries, circuit breakers, and automated failover drills \u2014 is needed regardless and is independent of fleet size.\n\n**Opportunity cost and reversibility**\n\nThe $180K we would spend on hardware is money not spent on hiring, on evaluation infrastructure, or simply preserved as runway. Hardware purchases are also poorly reversible: GPUs depreciate fast, resale is messy, and colocation contracts lock us in. Utilisation work, by contrast, is almost perfectly reversible \u2014 if a batching change hurts latency, we roll it back in an afternoon. When one option is reversible and the other is not, and both address the same symptom, we should exhaust the reversible one first. This is a decision under uncertainty, and the asymmetry favours waiting.\n\n**What would change my mind**\n\nIf our demand forecast shows sustained growth that utilisation gains cannot absorb, or if a strategic commitment (a large customer, a latency-sensitive product launch) makes tail-latency risk unacceptable, we should buy \u2014 and we should buy with headroom rather than at the margin, because incremental GPU purchases are operationally more expensive than step changes. I have asked Priya to draft a utilisation-improvement plan with weekly checkpoints; we will present measured results in six weeks.\n\n**Decision requested:** Endorse the utilisation-first approach and the six-week checkpoint before any capital approval.", + "output_characters": 4773, + "output_sha256": "4261741abeb86275bbb1f2c0c55e5818ae64eee22f8d319c8a39e61e3aa6fd0b", + "prompt_tokens": 123, + "reasoning_characters": 60, + "reasoning_sha256": "5e97dec2169fe02d6c9b226ef26dd5483486f54c386307d449ebfd382f63864e", + "response_tail_seconds": 0.008904, + "stall": { + "arrival_window_gaps": 13, + "arrival_window_max_gap_seconds": 3.139318, + "arrival_window_p95_gap_seconds": 3.127438, + "events": 963, + "max_gap_seconds": 3.472998, + "median_gap_seconds": 0.07204, + "p95_gap_seconds": 0.118349, + "spanning_gap_seconds": 0.106462 + }, + "started_monotonic_seconds": 360145.985478, + "stream": 2, + "stream_done_marker": true, + "stream_sha256": 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improving utilisation of the hardware we already own before committing to another purchase. The case is not that our GPUs are underused in some abstract sense \u2014 it is that the cheapest, fastest, and most reversible path to more capacity runs through scheduling, batching, and model-serving improvements, while a new purchase locks in cost and forecloses options we may want in six months.\n\n**Where we actually stand**\n\nOur current fleet runs at roughly 30\u201340% utilisation during business hours and drops sharply overnight and on weekends. That is not unusual, but it means that when we say we are \"out of capacity,\" what we really mean is that we are out of capacity at peak, for specific workloads, in a specific availability zone. The queueing we see on the shared inference cluster is concentrated in a handful of hours and a handful of models. Meanwhile, batch jobs that could run at any time are competing for the same resources as latency-sensitive interactive traffic.\n\nThe obvious fix \u2014 buy more cards \u2014 would work. It would also cost real money, take weeks to procure and integrate, consume rack power and cooling headroom we have not fully budgeted for, and, critically, commit us to a hardware generation at a moment when the model landscape is shifting under our feet. If the mix of workloads changes \u2014 say, smaller distilled models become viable for most of our traffic, or a provider's API becomes cheap enough to offload burst demand \u2014 a fleet sized for today's peak becomes an expensive liability rather than an asset.\n\n**Cost, honestly framed**\n\nThe purchase is not just the sticker price. It is depreciation on hardware that loses resale value quickly, the engineering time to integrate it, the operational burden of monitoring and maintaining it, and the opportunity cost of the same budget spent on the utilisation work itself. Utilisation work, by contrast, is mostly engineering time we would spend anyway: better batching, continuous batching in the serving layer, priority-aware scheduling, moving batch inference to off-peak windows, and quantisation where quality tolerances allow. None of it is glamorous, and some of it will fail \u2014 but the failures are cheap and teach us things we would need to know before scaling the fleet anyway.\n\n**Latency and reliability**\n\nThere is a real tension here that deserves acknowledgment. Aggressive consolidation \u2014 packing more tenants onto the same hardware, right-sizing reserve capacity \u2014 can degrade tail latency and reduce our slack for failures. Right now, our spare capacity is doing double duty as our reliability margin. If we squeeze utilisation up to 70\u201380%, a single node failure or a traffic spike lands harder. This is the strongest argument for buying, and I do not want to dismiss it: hardware redundancy is a simple, legible form of insurance.\n\nBut the response is not to buy blindly; it is to do the utilisation work with explicit latency and reliability budgets. If we can lift steady-state utilisation to 60% while holding p99 latency flat, we have effectively added capacity at near-zero capital cost. If we find that the last 20% of utilisation is only reachable by unacceptable latency or reliability trade-offs, that finding itself is the business case for the purchase \u2014 and we will make it with data instead of intuition.\n\n**Reversibility**\n\nThis is the decisive factor for me. Utilisation improvements are reversible and modular: we can stop, adjust, or roll back any piece of them. A hardware purchase is largely irreversible on our timescale. Once bought, the cards sit on our books whether or not the assumptions behind the purchase hold. In a period where model sizes, serving techniques, and hosted-API pricing are all moving quickly, optionality has real value. Buying now spends that optionality to solve a problem we can probably solve more cheaply another way.\n\n**What I propose**\n\nWe commit the next eight to ten weeks to the utilisation programme, with a concrete target: hold p99 latency at current levels while raising fleet utilisation from roughly 35% to 60%. We define the reliability guardrails up front so we are not trading away slack invisibly. At the end of that window, we revisit the purchase decision with measured numbers. If the data says we still need hardware \u2014 and it may \u2014 we will buy with confidence, having already banked the efficiency gains.\n\n**Uncertainty**\n\nI want to be candid about the risks in this plan. Utilisation projects have a habit of expanding; the 60% target may prove harder than it looks, and the engineering time is not free. There is also a scenario where demand grows faster than efficiency gains can absorb, and we end up buying under time pressure on worse terms. 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The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. 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After working through the numbers and, more importantly, thinking about what kind of team we want to be, my recommendation is that we spend the next quarter improving utilisation first, and defer any purchase decision until we have hard evidence that our existing fleet is genuinely saturated. I want to walk through the reasoning, because the argument is less about money than it is about what we can undo later.\n\n**The cost picture is less one-sided than it looks**\n\nThe obvious argument for buying is that hardware prices have stabilised and our queue times are occasionally annoying. But the sticker price of new accelerators is only the visible portion of the cost. 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Inference queues back up during peak hours, our fine-tuning experiments wait days for a slot, and two product teams have quietly started routing workloads to external APIs because our own capacity couldn't guarantee turnaround. The obvious question on the table is whether we should buy more hardware. My recommendation, after looking at the numbers and talking to everyone affected, is that we should not buy more hardware yet \u2014 we should first fix how we use what we already own, and revisit the purchase decision in one quarter with better data.\n\nThe case for buying is not unreasonable. Our fleet runs at roughly 38% average utilisation, but that average hides the real problem: peak demand sits near saturation while overnight and weekend usage is nearly zero. If we only looked at the average, we'd conclude we have headroom. 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This is not a compromise for its own sake; it reflects our honest read of the evidence, which is that our constraint is scheduling and workload placement, not raw capacity \u2014 but with enough uncertainty that we should not treat the question as closed.\n\n**What the data actually shows**\n\nOur GPU cluster's headline utilisation looks respectable, but the number is misleading. A large share of occupied hours come from jobs that are either idle-waiting on data, holding memory they no longer need, or running at batch sizes far below what the hardware could sustain. 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This is not a way of deferring a hard choice; it is the choice that keeps the most options open at the lowest cost, and it is the only path that lets us make the eventual buy decision with real data rather than extrapolation.\n\n**Where we actually stand**\n\nOur current cluster runs at roughly 35\u201340% utilisation measured over a full week, but that average hides the real problem: utilisation is bursty and poorly scheduled. Overnight and weekend capacity sits nearly idle, while midday demand from the fine-tuning queue regularly collides with inference traffic, producing the latency spikes that prompted this discussion in the first place. 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The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. The repository contains a service, tests, documentation, and release notes. Each observation records behaviour, evidence, constraints, and the next action. 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Queue times during peak hours have crept from under a second to somewhere between four and eight seconds, and two of our internal tools have started timing out intermittently. The obvious question on the table is whether we should buy more GPUs or find ways to squeeze more out of the fleet we already own. I want to lay out how I'm thinking about this trade-off and make a recommendation, while being honest about where my confidence is low.\n\nThe case for buying hardware is straightforward. Our utilization numbers are genuinely high \u2014 we're not leaving obvious capacity on the table in the way teams sometimes imagine. Batch sizes are already tuned, our serving layer does continuous batching, and the models we run are quantized to the point where further compression would start costing us output quality that users have actually noticed in testing. 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This memo lays out my recommendation and the reasoning behind it, with the caveat that some of what follows is judgement rather than certainty.\n\nLet me start with the case that's easiest to measure: cost. Our current four-GPU node was purchased at a moment when prices were elevated, and replacing or expanding it at today's prices is materially cheaper per unit of compute than what we paid. That fact alone makes the purchase option attractive, and it's tempting to stop the analysis there. But the purchase price is only the visible portion. New hardware carries depreciation, power, cooling, rack space, and \u2014 less obviously \u2014 the operational cost of managing a larger, more heterogeneous estate. Every additional device is something we must monitor, patch, schedule around, and eventually retire. Utilisation improvements, by contrast, are mostly paid for in", + "output_characters": 1226, + "output_sha256": "dbfca14e0ec2032222cee4ba35d190b1532de7e0d34fad4e5fb82f4f4895dcc6", + "prompt_tokens": 121, + "reasoning_characters": 62, + "reasoning_sha256": "7eddaec104351da3aba6059600be80b14d17920bd2df8ba3f1ac3d20ffb662d1", + "response_tail_seconds": 0.010597, + "started_monotonic_seconds": 360219.598201, + "stream": 2, + "stream_done_marker": true, + "stream_sha256": "a22d142f47e224ed95c8cf4804d1256d2e50bb9447d5dfb09a269c77111a6c86", + "time_to_first_visible_seconds": 14.171849, + "time_to_last_output_seconds": 34.520565, + "ttft_seconds": 2.835995, + "wall_seconds": 34.531162 + }, + { + "completion_tokens": 256, + "decode_seconds": 31.911921, + "decode_tokens_per_second": 7.991, + "event_seconds": [ + 3.529672, + 7.327724, + 10.440237, + 13.899479, + 13.899539, + 13.899556, + 13.899578, + 13.899593, + 14.024282, + 14.024327, + 14.024343, + 14.024356, + 14.024369, + 14.171046, + 14.171091, + 14.171108, + 14.3176, + 14.317667, + 14.478997, + 14.479039, + 14.620381, + 14.793345, + 14.79339, + 14.793405, + 14.961094, + 14.961121, + 15.146817, + 15.146842, + 15.146857, + 15.326473, + 15.32651, + 15.49852, + 15.498566, + 15.498582, + 15.498595, + 15.498608, + 15.498619, + 15.682099, + 15.682128, + 15.682143, + 15.682174, + 15.682187, + 15.864512, + 16.052053, + 16.052092, + 16.052106, + 16.228941, + 16.228968, + 16.421876, + 16.421921, + 16.598703, + 16.774539, + 16.774582, + 16.774598, + 16.77461, + 16.9567, + 16.956741, + 16.956757, + 17.144431, + 17.32571, + 17.506932, + 17.50699, + 17.507007, + 17.507019, + 17.687474, + 17.687516, + 17.868996, + 17.869046, + 17.869061, + 18.056109, + 18.056144, + 18.239256, + 18.431824, + 18.431873, + 18.431888, + 18.610527, + 18.610563, + 18.791137, + 18.973373, + 18.973406, + 19.163643, + 19.163685, + 19.1637, + 19.35142, + 19.351458, + 19.529052, + 19.529095, + 19.711311, + 19.904956, + 19.904994, + 20.089937, + 20.274613, + 20.465397, + 20.64858, + 20.838989, + 21.028057, + 21.028087, + 21.221287, + 21.221318, + 21.40989, + 21.612238, + 21.612306, + 21.612321, + 21.612334, + 21.801958, + 21.802007, + 21.989327, + 22.176545, + 22.176589, + 22.359687, + 22.550122, + 22.729433, + 22.894916, + 23.078603, + 23.078641, + 23.078656, + 23.078669, + 23.078681, + 23.078692, + 23.268218, + 23.268259, + 23.405403, + 23.405429, + 23.597129, + 23.78706, + 23.78709, + 23.787106, + 23.787119, + 23.78713, + 23.787166, + 23.787178, + 23.972947, + 23.972986, + 24.152337, + 24.335577, + 24.335613, + 24.511132, + 24.697313, + 24.885696, + 24.885732, + 25.074643, + 25.074676, + 25.25642, + 25.256449, + 25.446604, + 25.637207, + 25.637254, + 25.63727, + 25.818536, + 25.818593, + 25.818609, + 25.818622, + 26.005772, + 26.183943, + 26.367852, + 26.367885, + 26.550461, + 26.550501, + 26.743138, + 26.743197, + 26.743213, + 26.743226, + 26.919968, + 26.920011, + 26.920026, + 27.103486, + 27.10353, + 27.2874, + 27.471512, + 27.471546, + 27.658413, + 27.843227, + 27.84327, + 28.027797, + 28.205129, + 28.390667, + 28.390717, + 28.390733, + 28.577011, + 28.77462, + 28.963434, + 29.154798, + 29.344309, + 29.344345, + 29.344362, + 29.344376, + 29.344387, + 29.540964, + 29.540992, + 29.541008, + 29.541023, + 29.541035, + 29.728366, + 29.909585, + 29.909637, + 29.909653, + 29.909668, + 29.90968, + 29.909692, + 30.092081, + 30.269122, + 30.449737, + 30.629089, + 30.822906, + 30.822963, + 31.009421, + 31.188933, + 31.375515, + 31.37555, + 31.571523, + 31.571562, + 31.756542, + 31.756582, + 31.942912, + 32.129919, + 32.301491, + 32.301534, + 32.301549, + 32.301562, + 32.490186, + 32.49021, + 32.676192, + 32.867727, + 32.867755, + 33.053648, + 33.053688, + 33.053704, + 33.053717, + 33.053729, + 33.053742, + 33.053753, + 33.239343, + 33.430618, + 33.430682, + 33.616335, + 33.616373, + 33.616391, + 33.616404, + 33.616415, + 33.80525, + 33.805286, + 33.987894, + 34.177032, + 34.177067, + 34.177081, + 34.355822, + 34.355864, + 34.52022, + 34.682045, + 34.844565, + 35.003424, + 35.157822, + 35.157864, + 35.310532, + 35.441593 + ], + "finish_reason": "length", + "finished_monotonic_seconds": 360255.046742, + "first_output_monotonic_seconds": 360223.127523, + "measured_sse_events": 255, + "output": "**MEMO: GPU Capacity \u2014 Buy More or Use What We Have Better?**\n\n**To:** Engineering Leadership\n**From:** Infrastructure Team\n**Re:** Inference capacity planning for the next two quarters\n\n**Summary and recommendation**\n\nWe recommend that we spend the next quarter improving utilisation of our existing GPU fleet before committing to any new hardware purchases. If utilisation work stalls or product demand accelerates faster than our current forecast, we should revisit procurement with a target decision date roughly ninety days out. This is not a deferral for its own sake; it is a deliberate sequencing choice that we believe maximises information value and minimises irreversible spend.\n\n**Where we stand**\n\nOur four A100 nodes currently run at roughly 38 percent average utilisation, but that number hides a lumpy reality. Peak demand between 9 a.m. and 1 p.m. pushes two of the nodes to saturation, and we have seen queue times for batch inference jobs stretch past forty minutes on several occasions. Meanwhile, overnight and weekend utilisation drops below 15 percent. The problem, in other words, is not raw capacity but the shape of our demand curve against a fixed supply. Buying a fifth", + "output_characters": 1197, + "output_sha256": "11dad4d670e4d8d266570146bcff281ad8365381c5ef89cb019df922ed712ada", + "prompt_tokens": 119, + "reasoning_characters": 62, + "reasoning_sha256": "7eddaec104351da3aba6059600be80b14d17920bd2df8ba3f1ac3d20ffb662d1", + "response_tail_seconds": 0.007298, + "started_monotonic_seconds": 360219.597851, + "stream": 3, + "stream_done_marker": true, + "stream_sha256": "07285dc3a55d259b941fbb80a5cdd33bd98d7d76223366a1715e974a3944ed66", + "time_to_first_visible_seconds": 14.171108, + "time_to_last_output_seconds": 35.441593, + "ttft_seconds": 3.529672, + "wall_seconds": 35.448891 + } + ], + "newcomers_started_during_prefill": 3, + "overlap_valid": true, + "round": 2, + "short_solo_ttft_seconds": 0.739031, + "solo": { + "completion_tokens": 256, + "decode_seconds": 6.415718, + "decode_tokens_per_second": 39.746, + "event_seconds": [ + 0.739031, + 0.812764, + 0.861039, + 0.914557, + 0.914592, + 0.914608, + 0.914621, + 0.961985, + 0.962029, + 1.016641, + 1.016675, + 1.01669, + 1.016702, + 1.067318, + 1.06735, + 1.067365, + 1.116134, + 1.116177, + 1.161776, + 1.20954, + 1.209573, + 1.209587, + 1.255532, + 1.255568, + 1.301727, + 1.301769, + 1.301786, + 1.363105, + 1.363163, + 1.36318, + 1.363194, + 1.410028, + 1.460998, + 1.461032, + 1.461047, + 1.509938, + 1.509977, + 1.556354, + 1.556392, + 1.610077, + 1.610135, + 1.660284, + 1.660344, + 1.729012, + 1.791705, + 1.791736, + 1.791751, + 1.791765, + 1.791776, + 1.84033, + 1.894026, + 1.89406, + 1.942213, + 1.942256, + 1.993473, + 2.042424, + 2.093259, + 2.093294, + 2.14042, + 2.189545, + 2.189584, + 2.189599, + 2.248655, + 2.248685, + 2.29608, + 2.29611, + 2.344661, + 2.398655, + 2.39871, + 2.398725, + 2.448102, + 2.448139, + 2.448154, + 2.496903, + 2.546016, + 2.593612, + 2.643247, + 2.643282, + 2.643297, + 2.691574, + 2.691606, + 2.739703, + 2.739736, + 2.789434, + 2.789464, + 2.839706, + 2.839736, + 2.891955, + 2.891985, + 2.892, + 2.945142, + 2.945171, + 2.994144, + 2.99419, + 2.994205, + 3.043027, + 3.043066, + 3.09335, + 3.144181, + 3.193316, + 3.250534, + 3.306958, + 3.362642, + 3.36268, + 3.362695, + 3.362708, + 3.410329, + 3.459338, + 3.509079, + 3.509114, + 3.50913, + 3.559428, + 3.609423, + 3.658107, + 3.658142, + 3.708206, + 3.708237, + 3.708252, + 3.769906, + 3.769939, + 3.769954, + 3.769967, + 3.817898, + 3.817931, + 3.866474, + 3.866506, + 3.915675, + 3.965853, + 3.965906, + 3.965927, + 4.025974, + 4.026008, + 4.026022, + 4.073851, + 4.123401, + 4.123438, + 4.123453, + 4.172575, + 4.172612, + 4.172627, + 4.221551, + 4.270024, + 4.318569, + 4.318603, + 4.367789, + 4.367853, + 4.415854, + 4.415909, + 4.415924, + 4.466344, + 4.519613, + 4.519644, + 4.567579, + 4.567627, + 4.616109, + 4.616139, + 4.665057, + 4.714361, + 4.764178, + 4.76423, + 4.764245, + 4.812664, + 4.812698, + 4.862242, + 4.910314, + 4.961506, + 5.013962, + 5.063991, + 5.109104, + 5.109149, + 5.109163, + 5.15736, + 5.219955, + 5.219993, + 5.220009, + 5.220022, + 5.267028, + 5.313567, + 5.313601, + 5.313616, + 5.361419, + 5.361456, + 5.361471, + 5.409593, + 5.409641, + 5.409656, + 5.457688, + 5.506929, + 5.554703, + 5.554746, + 5.612245, + 5.612274, + 5.612289, + 5.612303, + 5.612314, + 5.665473, + 5.66553, + 5.665545, + 5.714091, + 5.762127, + 5.808049, + 5.808097, + 5.808111, + 5.865129, + 5.86516, + 5.914231, + 5.914267, + 5.963735, + 6.012989, + 6.013032, + 6.013046, + 6.062688, + 6.06272, + 6.111126, + 6.111163, + 6.159705, + 6.159757, + 6.208753, + 6.208833, + 6.259892, + 6.306162, + 6.364402, + 6.36444, + 6.364455, + 6.412431, + 6.412472, + 6.462835, + 6.462878, + 6.462893, + 6.509821, + 6.509854, + 6.557023, + 6.557076, + 6.557096, + 6.605641, + 6.654247, + 6.654277, + 6.702738, + 6.75091, + 6.750951, + 6.799368, + 6.799414, + 6.850083, + 6.850113, + 6.850128, + 6.897755, + 6.897795, + 6.946622, + 6.995484, + 7.052368, + 7.052399, + 7.052414, + 7.099, + 7.09903, + 7.15475 + ], + "finish_reason": "length", + "finished_monotonic_seconds": 360218.593959, + "first_output_monotonic_seconds": 360212.175222, + "measured_sse_events": 255, + "output": "**MEMO**\n\n**To:** Platform Engineering Team\n**From:** Infrastructure Planning\n**Re:** Buy more GPU hardware, or get more out of what we have?\n**Status:** For discussion, decision requested by end of month\n\nWe've reached the point where our inference cluster is saturated during business hours and our batch jobs are queuing into the evening. The instinctive response is to buy more cards. Before we commit, I want to argue that the better move, at least for the next two quarters, is to invest in utilisation: better scheduling, batching, quantisation where it's acceptable, and a serious look at the idle capacity we've been ignoring.\n\nStart with cost. A meaningful hardware expansion \u2014 enough to relieve our peak-hour contention rather than just paper over it \u2014 would run to roughly $180K in capital, plus the operational overhead of power, cooling, and rack space that our facilities team has already flagged as tight. Against that, the utilisation work is mostly engineering time: perhaps six to eight weeks across two of us, plus a modest budget for a commercial scheduler if the open-source options fall short. Our current fleet averages something like 35% utilisation", + "output_characters": 1174, + "output_sha256": "5dfa2dcfb662a915223751023fa0b3f02e2d2d2215d6a0d6e0740d6ebfbb93fd", + "prompt_tokens": 124, + "reasoning_characters": 67, + "reasoning_sha256": "534bd8205f1c9b08ab4e9db6347d5c54d56a7224c969af4cb9f5ee2b9e7b8bed", + "response_tail_seconds": 0.003019, + "started_monotonic_seconds": 360211.436191, + "stream_done_marker": true, + "stream_sha256": "23f1cd3e8bdc4258532f11db59e23945f1713450387c45ce14b80ad94e9d3543", + "time_to_first_visible_seconds": 1.116134, + "time_to_last_output_seconds": 7.15475, + "ttft_seconds": 0.739031, + "wall_seconds": 7.157768 + } + } + ], + "total_rounds": 2, + "valid_rounds": 2 + } + } + } +} diff --git a/results/2026-10-12-rc4/raw/rig-decode.json b/results/2026-10-12-rc4/raw/rig-decode.json new file mode 100644 index 0000000..ab953ed --- /dev/null +++ b/results/2026-10-12-rc4/raw/rig-decode.json @@ -0,0 +1,765 @@ +{ + "decode": { + "code": { + "completion_gate": { + "passed": 3, + "total": 3 + }, + "decode_tokens_per_second": { + "maximum": 72.374, + "median": 72.252, + "minimum": 68.75, + "p90": 72.374 + }, + "runs": [ + { + "completion_tokens": 2172, + "completion_validation": { + "valid": true + }, + "decode_seconds": 31.577984, + "decode_tokens_per_second": 68.75, + "finish_reason": "stop", + "measured_sse_events": 2170, + "output": "```go\n// ratelimit.go\npackage ratelimit\n\nimport (\n\t\"errors\"\n\t\"fmt\"\n\t\"sync\"\n\t\"time\"\n)\n\n// Clock abstracts time for testability.\ntype Clock interface {\n\tNow() time.Time\n}\n\n// realClock uses the standard library.\ntype realClock struct{}\n\nfunc (realClock) Now() time.Time { return time.Now() }\n\n// Limiter is a concurrency-safe token-bucket rate limiter.\n// A zero value is not usable; construct with NewLimiter.\ntype Limiter struct {\n\tmu sync.Mutex\n\trate float64 // tokens per second\n\tburst float64 // bucket capacity\n\ttokens float64 // current tokens\n\tlast time.Time // last refill time\n\tclock Clock\n}\n\n// NewLimiter creates a limiter that refills rate tokens per second,\n// with a maximum capacity of burst tokens.\nfunc NewLimiter(rate float64, burst int) (*Limiter, error) {\n\treturn NewLimiterWithClock(rate, burst, realClock{})\n}\n\n// NewLimiterWithClock is like NewLimiter with an injectable clock.\nfunc NewLimiterWithClock(rate float64, burst int, clock Clock) (*Limiter, error) {\n\tif clock == nil {\n\t\treturn nil, errors.New(\"ratelimit: nil clock\")\n\t}\n\tif rate <= 0 {\n\t\treturn nil, fmt.Errorf(\"ratelimit: rate must be > 0, got %v\", rate)\n\t}\n\tif burst <= 0 {\n\t\treturn nil, fmt.Errorf(\"ratelimit: burst must be > 0, got %d\", burst)\n\t}\n\treturn &Limiter{\n\t\trate: rate,\n\t\tburst: float64(burst),\n\t\ttokens: float64(burst), // start full\n\t\tlast: clock.Now(),\n\t\tclock: clock,\n\t}, nil\n}\n\n// advance refills tokens based on elapsed time. Caller must hold mu.\nfunc (l *Limiter) advance(now time.Time) {\n\tif now.After(l.last) {\n\t\telapsed := now.Sub(l.last).Seconds()\n\t\tl.tokens = min(l.burst, l.tokens+elapsed*l.rate)\n\t\tl.last = now\n\t}\n}\n\n// AllowN reports whether n tokens may be consumed at time now.\n// If allowed, the tokens are consumed.\nfunc (l *Limiter) AllowN(now time.Time, n int) bool {\n\tif n <= 0 {\n\t\treturn false\n\t}\n\tl.mu.Lock()\n\tdefer l.mu.Unlock()\n\tl.advance(now)\n\tneed := float64(n)\n\tif l.tokens >= need {\n\t\tl.tokens -= need\n\t\treturn true\n\t}\n\treturn false\n}\n\n// Allow reports whether a single token may be consumed now.\nfunc (l *Limiter) Allow() bool {\n\treturn l.AllowN(l.clock.Now(), 1)\n}\n\n// Tokens returns the number of tokens currently available (approximate,\n// as of the current clock reading).\nfunc (l *Limiter) Tokens() float64 {\n\tl.mu.Lock()\n\tdefer l.mu.Unlock()\n\tl.advance(l.clock.Now())\n\treturn l.tokens\n}\n\nfunc min(a, b float64) float64 {\n\tif a < b {\n\t\treturn a\n\t}\n\treturn b\n}\n```\n\n```go\n// ratelimit_test.go\npackage ratelimit\n\nimport (\n\t\"testing\"\n\t\"time\"\n)\n\ntype fakeClock struct {\n\tt time.Time\n}\n\nfunc (f *fakeClock) Now() time.Time { return f.t }\nfunc (f *fakeClock) Advance(d time.Duration) {\n\tf.t = f.t.Add(d)\n}\n\nfunc mustNew(t *testing.T, rate float64, burst int, c Clock) *Limiter {\n\tt.Helper()\n\tl, err := NewLimiterWithClock(rate, burst, c)\n\tif err != nil {\n\t\tt.Fatalf(\"NewLimiterWithClock(%v, %d): %v\", rate, burst, err)\n\t}\n\treturn l\n}\n\nfunc TestConstructorValidation(t *testing.T) {\n\ttests := []struct {\n\t\tname string\n\t\trate float64\n\t\tburst int\n\t\tclock Clock\n\t\twantErr bool\n\t}{\n\t\t{\"zero rate\", 0, 1, realClock{}, true},\n\t\t{\"negative rate\", -1.5, 1, realClock{}, true},\n\t\t{\"zero burst\", 1, 0, realClock{}, true},\n\t\t{\"negative burst\", 1, -3, realClock{}, true},\n\t\t{\"nil clock\", 1, 1, nil, true},\n\t\t{\"valid\", 2.5, 5, realClock{}, false},\n\t}\n\tfor _, tc := range tests {\n\t\tt.Run(tc.name, func(t *testing.T) {\n\t\t\t_, err := NewLimiterWithClock(tc.rate, tc.burst, tc.clock)\n\t\t\tif (err != nil) != tc.wantErr {\n\t\t\t\tt.Fatalf(\"err = %v, wantErr = %v\", err, tc.wantErr)\n\t\t\t}\n\t\t})\n\t}\n}\n\nfunc TestAllowN(t *testing.T) {\n\tbase := time.Date(2024, 1, 1, 0, 0, 0, 0, time.UTC)\n\ttests := []struct {\n\t\tname string\n\t\trate float64\n\t\tburst int\n\t\tsteps []struct {\n\t\t\tadvance time.Duration\n\t\t\tn int\n\t\t\twant bool\n\t\t}\n\t}{\n\t\t{\n\t\t\tname: \"burst exhausted then denied\",\n\t\t\trate: 1, burst: 2,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{0, 2, true},\n\t\t\t\t{0, 1, false},\n\t\t\t},\n\t\t},\n\t\t{\n\t\t\tname: \"refill over time\",\n\t\t\trate: 1, burst: 2,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{0, 2, true},\n\t\t\t\t{0, 1, false},\n\t\t\t\t{time.Second, 1, true},\n\t\t\t\t{0, 1, false},\n\t\t\t\t{2 * time.Second, 2, true},\n\t\t\t\t{0, 1, false},\n\t\t\t},\n\t\t},\n\t\t{\n\t\t\tname: \"burst caps refill\",\n\t\t\trate: 10, burst: 2,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{0, 2, true},\n\t\t\t\t{10 * time.Second, 3, false}, // capped at 2\n\t\t\t\t{0, 2, true},\n\t\t\t},\n\t\t},\n\t\t{\n\t\t\tname: \"fractional refill\",\n\t\t\trate: 2, burst: 1,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{0, 1, true},\n\t\t\t\t{250 * time.Millisecond, 1, false}, // 0.5 tokens\n\t\t\t\t{250 * time.Millisecond, 1, true}, // 1.0 tokens\n\t\t\t},\n\t\t},\n\t\t{\n\t\t\tname: \"zero and negative n denied\",\n\t\t\trate: 1, burst: 1,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{0, 0, false},\n\t\t\t\t{0, -1, false},\n\t\t\t\t{0, 1, true},\n\t\t\t},\n\t\t},\n\t\t{\n\t\t\tname: \"request larger than burst never allowed\",\n\t\t\trate: 1, burst: 1,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{time.Hour, 2, false},\n\t\t\t},\n\t\t},\n\t}\n\n\tfor _, tc := range tests {\n\t\tt.Run(tc.name, func(t *testing.T) {\n\t\t\tfc := &fakeClock{t: base}\n\t\t\tl := mustNew(t, tc.rate, tc.burst, fc)\n\t\t\tfor i, s := range tc.steps {\n\t\t\t\tfc.Advance(s.advance)\n\t\t\t\tif got := l.AllowN(fc.Now(), s.n); got != s.want {\n\t\t\t\t\tt.Fatalf(\"step %d: AllowN(%v) = %v, want %v\", i, s.n, got, s.want)\n\t\t\t\t}\n\t\t\t}\n\t\t})\n\t}\n}\n\nfunc TestTokensRefill(t *testing.T) {\n\tfc := &fakeClock{t: time.Now()}\n\tl := mustNew(t, 1, 5, fc)\n\tl.AllowN(fc.t, 5)\n\tif got := l.Tokens(); got != 0 {\n\t\tt.Fatalf(\"tokens after drain = %v, want 0\", got)\n\t}\n\tfc.Advance(2 * time.Second)\n\tif got := l.Tokens(); got != 2 {\n\t\tt.Fatalf(\"tokens after 2s = %v, want 2\", got)\n\t}\n}\n\nfunc TestConcurrentAccess(t *testing.T) {\n\tfc := &fakeClock{t: time.Now()}\n\tl := mustNew(t, 1000, 1000, fc)\n\tdone := make(chan struct{})\n\tvar allowed int64\n\tvar mu sync.Mutex\n\tfor i := 0; i < 10; i++ {\n\t\tgo func() {\n\t\t\tdefer close(done)\n\t\t\tfor j := 0; j < 100; j++ {\n\t\t\t\tif l.AllowN(fc.Now(), 1) {\n\t\t\t\t\tmu.Lock()\n\t\t\t\t\tallowed++\n\t\t\t\t\tmu.Unlock()\n\t\t\t\t}\n\t\t\t}\n\t\t\tdone <- struct{}{}\n\t\t}()\n\t}\n\tfor i := 0; i < 10; i++ {\n\t\t<-done\n\t}\n\tif allowed > 1000 {\n\t\tt.Fatalf(\"allowed %d, exceeds burst 1000\", allowed)\n\t}\n}\n```\n\nNote: the test file needs `import \"sync\"` added to the import block (`sync` is used in `TestConcurrentAccess`).", + "output_characters": 6499, + "output_sha256": "7cee4b4018a96229ca3c68b566a2d229c881560c48627bb2d38cd96f287048da", + "prompt_tokens": 129, + "reasoning_characters": 27, + "reasoning_sha256": "0b3acc8403ec469b09b04be516f98e059799ba61dabe4c94250bbf24a6f23331", + "response_tail_seconds": 0.002975, + "run": 1, + "stream_done_marker": true, + "stream_sha256": "139f9d65b6f198abbd901e5c40312e9c1a1674283cb9024a5b3701e6074c04f7", + "time_to_first_visible_seconds": 0.955459, + "time_to_last_output_seconds": 32.299618, + "ttft_seconds": 0.721633, + "wall_seconds": 32.302593 + }, + { + "completion_tokens": 2360, + "completion_validation": { + "valid": true + }, + "decode_seconds": 32.649728, + "decode_tokens_per_second": 72.252, + "finish_reason": "stop", + "measured_sse_events": 2358, + "output": "**ratelimit.go**\n```go\n// Package ratelimit implements a concurrency-safe token-bucket rate limiter.\npackage ratelimit\n\nimport (\n\t\"errors\"\n\t\"fmt\"\n\t\"sync\"\n\t\"time\"\n)\n\n// Clock abstracts time for testability.\ntype Clock interface {\n\tNow() time.Time\n}\n\ntype realClock struct{}\n\nfunc (realClock) Now() time.Time { return time.Now() }\n\n// Limiter is a token-bucket rate limiter. It is safe for concurrent use.\ntype Limiter struct {\n\tmu sync.Mutex\n\trate float64 // tokens per second\n\tburst float64 // bucket capacity\n\ttokens float64 // current tokens\n\tlast time.Time // last refill time\n\tclock Clock\n}\n\n// NewLimiter creates a Limiter that refills at rate tokens per second with a\n// maximum burst capacity. Both rate and burst must be > 0.\nfunc NewLimiter(rate, burst float64) (*Limiter, error) {\n\treturn NewLimiterWithClock(rate, burst, realClock{})\n}\n\n// NewLimiterWithClock is NewLimiter with an injectable Clock.\nfunc NewLimiterWithClock(rate, burst float64, c Clock) (*Limiter, error) {\n\tif c == nil {\n\t\treturn nil, errors.New(\"ratelimit: nil clock\")\n\t}\n\tif rate <= 0 {\n\t\treturn nil, fmt.Errorf(\"ratelimit: rate must be > 0, got %v\", rate)\n\t}\n\tif burst <= 0 {\n\t\treturn nil, fmt.Errorf(\"ratelimit: burst must be > 0, got %v\", burst)\n\t}\n\tnow := c.Now()\n\treturn &Limiter{\n\t\trate: rate,\n\t\tburst: burst,\n\t\ttokens: burst,\n\t\tlast: now,\n\t\tclock: c,\n\t}, nil\n}\n\n// Allow reports whether a single token is available.\nfunc (l *Limiter) Allow() bool {\n\treturn l.AllowN(1)\n}\n\n// AllowN reports whether n tokens are available at time now, consuming them\n// if so. n must be >= 1; a non-positive n returns false.\nfunc (l *Limiter) AllowN(n int) bool {\n\tif n <= 0 {\n\t\treturn false\n\t}\n\tl.mu.Lock()\n\tdefer l.mu.Unlock()\n\n\tnow := l.clock.Now()\n\telapsed := now.Sub(l.last).Seconds()\n\tif elapsed > 0 {\n\t\tl.tokens = min(l.burst, l.tokens+elapsed*l.rate)\n\t\tl.last = now\n\t}\n\tif l.tokens < float64(n) {\n\t\treturn false\n\t}\n\tl.tokens -= float64(n)\n\treturn true\n}\n\n// Tokens returns the number of tokens currently available.\nfunc (l *Limiter) Tokens() float64 {\n\tl.mu.Lock()\n\tdefer l.mu.Unlock()\n\telapsed := l.clock.Now().Sub(l.last).Seconds()\n\tif elapsed > 0 {\n\t\treturn min(l.burst, l.tokens+elapsed*l.rate)\n\t}\n\treturn l.tokens\n}\n\nfunc min(a, b float64) float64 {\n\tif a < b {\n\t\treturn a\n\t}\n\treturn b\n}\n```\n\n**ratelimit_test.go**\n```go\npackage ratelimit\n\nimport (\n\t\"errors\"\n\t\"testing\"\n\t\"time\"\n)\n\ntype fakeClock struct {\n\tt time.Time\n}\n\nfunc (c *fakeClock) Now() time.Time { return c.t }\nfunc (c *fakeClock) Advance(d time.Duration) {\n\tc.t = c.t.Add(d)\n}\n\nfunc TestNewLimiterValidation(t *testing.T) {\n\ttests := []struct {\n\t\tname string\n\t\trate float64\n\t\tburst float64\n\t\twantErr bool\n\t}{\n\t\t{\"valid\", 1, 1, false},\n\t\t{\"zero rate\", 0, 1, true},\n\t\t{\"negative rate\", -1, 1, true},\n\t\t{\"zero burst\", 1, 0, true},\n\t\t{\"negative burst\", 1, -1, true},\n\t}\n\tfor _, tc := range tests {\n\t\tt.Run(tc.name, func(t *testing.T) {\n\t\t\t_, err := NewLimiter(tc.rate, tc.burst)\n\t\t\tif (err != nil) != tc.wantErr {\n\t\t\t\tt.Fatalf(\"NewLimiter(%v, %v) err = %v, wantErr %v\", tc.rate, tc.burst, err, tc.wantErr)\n\t\t\t}\n\t\t})\n\t}\n\tt.Run(\"nil clock\", func(t *testing.T) {\n\t\t_, err := NewLimiterWithClock(1, 1, nil)\n\t\tif err == nil {\n\t\t\tt.Fatal(\"expected error for nil clock\")\n\t\t}\n\t})\n}\n\nfunc TestAllowN(t *testing.T) {\n\tstart := time.Date(2024, 1, 1, 0, 0, 0, 0, time.UTC)\n\ttests := []struct {\n\t\tname string\n\t\trate float64\n\t\tburst float64\n\t\tsteps []struct {\n\t\t\tadvance time.Duration\n\t\t\tn int\n\t\t\twant bool\n\t\t}\n\t}{\n\t\t{\n\t\t\tname: \"burst then deny\",\n\t\t\trate: 1,\n\t\t\tburst: 2,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{0, 1, true},\n\t\t\t\t{0, 1, true},\n\t\t\t\t{0, 1, false},\n\t\t\t},\n\t\t},\n\t\t{\n\t\t\tname: \"refill over time\",\n\t\t\trate: 1,\n\t\t\tburst: 1,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{0, 1, true},\n\t\t\t\t{0, 1, false},\n\t\t\t\t{time.Second, 1, true},\n\t\t\t\t{500 * time.Millisecond, 1, false},\n\t\t\t\t{500 * time.Millisecond, 1, true},\n\t\t\t},\n\t\t},\n\t\t{\n\t\t\tname: \"partial refill accumulates\",\n\t\t\trate: 2,\n\t\t\tburst: 2,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{0, 2, true},\n\t\t\t\t{0, 1, false},\n\t\t\t\t{250 * time.Millisecond, 1, false},\n\t\t\t\t{250 * time.Millisecond, 1, true},\n\t\t\t},\n\t\t},\n\t\t{\n\t\t\tname: \"tokens capped at burst\",\n\t\t\trate: 10,\n\t\t\tburst: 1,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{0, 1, true},\n\t\t\t\t{time.Hour, 1, true},\n\t\t\t\t{0, 1, false},\n\t\t\t},\n\t\t},\n\t\t{\n\t\t\tname: \"multi-token request\",\n\t\t\trate: 1,\n\t\t\tburst: 5,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{0, 3, true},\n\t\t\t\t{0, 3, false},\n\t\t\t\t{2 * time.Second, 3, true},\n\t\t\t\t{0, 1, false},\n\t\t\t},\n\t\t},\n\t\t{\n\t\t\tname: \"non-positive n denied\",\n\t\t\trate: 1,\n\t\t\tburst: 1,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{0, 0, false},\n\t\t\t\t{0, -1, false},\n\t\t\t\t{0, 1, true},\n\t\t\t},\n\t\t},\n\t}\n\tfor _, tc := range tests {\n\t\tt.Run(tc.name, func(t *testing.T) {\n\t\t\tclk := &fakeClock{t: start}\n\t\t\tl, err := NewLimiterWithClock(tc.rate, tc.burst, clk)\n\t\t\tif err != nil {\n\t\t\t\tt.Fatalf(\"NewLimiterWithClock: %v\", err)\n\t\t\t}\n\t\t\tfor i, s := range tc.steps {\n\t\t\t\tclk.Advance(s.advance)\n\t\t\t\tif got := l.AllowN(s.n); got != s.want {\n\t\t\t\t\tt.Fatalf(\"step %d: AllowN(%d) = %v, want %v\", i, s.n, got, s.want)\n\t\t\t\t}\n\t\t\t}\n\t\t})\n\t}\n}\n\nfunc TestAllow(t *testing.T) {\n\tclk := &fakeClock{t: time.Now()}\n\tl, err := NewLimiterWithClock(1, 1, clk)\n\tif err != nil {\n\t\tt.Fatalf(\"NewLimiterWithClock: %v\", err)\n\t}\n\tif !l.Allow() {\n\t\tt.Fatal(\"first Allow = false, want true\")\n\t}\n\tif l.Allow() {\n\t\tt.Fatal(\"second Allow = true, want false\")\n\t}\n\tclk.Advance(time.Second)\n\tif !l.Allow() {\n\t\tt.Fatal(\"Allow after refill = false, want true\")\n\t}\n}\n\nfunc TestTokens(t *testing.T) {\n\tclk := &fakeClock{t: time.Now()}\n\tl, err := NewLimiterWithClock(1, 2, clk)\n\tif err != nil {\n\t\tt.Fatalf(\"NewLimiterWithClock: %v\", err)\n\t}\n\tif got := l.Tokens(); got != 2 {\n\t\tt.Fatalf(\"Tokens = %v, want 2\", got)\n\t}\n\tl.AllowN(2)\n\tif got := l.Tokens(); got != 0 {\n\t\tt.Fatalf(\"Tokens = %v, want 0\", got)\n\t}\n\tclk.Advance(500 * time.Millisecond)\n\tif got := l.Tokens(); got != 0.5 {\n\t\tt.Fatalf(\"Tokens = %v, want 0.5\", got)\n\t}\n}\n\nfunc TestConcurrentUse(t *testing.T) {\n\tclk := &fakeClock{t: time.Now()}\n\tl, err := NewLimiterWithClock(1000, 100, clk)\n\tif err != nil {\n\t\tt.Fatalf(\"NewLimiterWithClock: %v\", err)\n\t}\n\tvar allowed int64\n\tvar mu = make(chan struct{}, 1)\n\tdone := make(chan struct{})\n\tfor i := 0; i < 100; i++ {\n\t\tgo func() {\n\t\t\tdefer func() { done <- struct{}{} }()\n\t\t\tif l.Allow() {\n\t\t\t\tmu <- struct{}{}\n\t\t\t\tallowed++\n\t\t\t\t<-mu\n\t\t\t}\n\t\t}()\n\t}\n\tfor i := 0; i < 100; i++ {\n\t\t<-done\n\t}\n\tif allowed > 100 {\n\t\tt.Fatalf(\"allowed = %d, exceeds burst 100\", allowed)\n\t}\n\tif allowed == 0 {\n\t\tt.Fatal(\"allowed = 0, want some allowed\")\n\t}\n}\n\nvar errUnused = errors.New(\"unused\")\n```", + "output_characters": 6913, + "output_sha256": "d427695c2c7936ee9555fc8f431b6205c385accd865a2f8204bd412d927d42be", + "prompt_tokens": 132, + "reasoning_characters": 16, + "reasoning_sha256": "7ba5de4fc5e8e3e55e8129959b54cb2c5e3d69131dc05fe5513bb7f6f348d9f1", + "response_tail_seconds": 0.054662, + "run": 2, + "stream_done_marker": true, + "stream_sha256": "0468335fd9439dbca9814c8551e411c2accd1ffac9ae1e6d0b7d5ee7077ecbe1", + "time_to_first_visible_seconds": 0.923007, + "time_to_last_output_seconds": 33.384104, + "ttft_seconds": 0.734376, + "wall_seconds": 33.438767 + }, + { + "completion_tokens": 1813, + "completion_validation": { + "valid": true + }, + "decode_seconds": 25.036721, + "decode_tokens_per_second": 72.374, + "finish_reason": "stop", + "measured_sse_events": 1811, + "output": "```go\n// ratelimit.go\npackage ratelimit\n\nimport (\n\t\"errors\"\n\t\"sync\"\n\t\"time\"\n)\n\n// Clock abstracts time for testability.\ntype Clock interface {\n\tNow() time.Time\n}\n\ntype realClock struct{}\n\nfunc (realClock) Now() time.Time { return time.Now() }\n\n// Limiter is a concurrency-safe token-bucket rate limiter.\ntype Limiter struct {\n\tmu sync.Mutex\n\trate float64 // tokens per second\n\tburst float64 // bucket capacity\n\ttokens float64\n\tlast time.Time\n\tclock Clock\n}\n\nvar ErrInvalidArg = errors.New(\"ratelimit: rate and burst must be positive\")\n\n// New creates a Limiter with the given refill rate (tokens/sec) and burst size.\nfunc New(rate float64, burst int) (*Limiter, error) {\n\treturn NewWithClock(rate, burst, realClock{})\n}\n\n// NewWithClock is like New but accepts an injectable Clock.\nfunc NewWithClock(rate float64, burst int, clock Clock) (*Limiter, error) {\n\tif rate <= 0 || burst <= 0 {\n\t\treturn nil, ErrInvalidArg\n\t}\n\tif clock == nil {\n\t\treturn nil, errors.New(\"ratelimit: nil clock\")\n\t}\n\tnow := clock.Now()\n\treturn &Limiter{\n\t\trate: rate,\n\t\tburst: float64(burst),\n\t\ttokens: float64(burst),\n\t\tlast: now,\n\t\tclock: clock,\n\t}, nil\n}\n\n// Allow reports whether a single token is available.\nfunc (l *Limiter) Allow() bool { return l.AllowN(1) }\n\n// AllowN reports whether n tokens are available, consuming them if so.\nfunc (l *Limiter) AllowN(n int) bool {\n\tif n <= 0 {\n\t\treturn false\n\t}\n\tl.mu.Lock()\n\tdefer l.mu.Unlock()\n\n\tnow := l.clock.Now()\n\telapsed := now.Sub(l.last).Seconds()\n\tif elapsed > 0 {\n\t\tl.tokens += elapsed * l.rate\n\t\tif l.tokens > l.burst {\n\t\t\tl.tokens = l.burst\n\t\t}\n\t\tl.last = now\n\t}\n\tif float64(n) <= l.tokens {\n\t\tl.tokens -= float64(n)\n\t\treturn true\n\t}\n\treturn false\n}\n\n// Tokens returns the current number of available tokens (approximate).\nfunc (l *Limiter) Tokens() float64 {\n\tl.mu.Lock()\n\tdefer l.mu.Unlock()\n\treturn l.tokens\n}\n```\n\n```go\n// ratelimit_test.go\npackage ratelimit\n\nimport (\n\t\"sync\"\n\t\"testing\"\n\t\"time\"\n)\n\ntype fakeClock struct {\n\tt time.Time\n}\n\nfunc (c *fakeClock) Now() time.Time { return c.t }\nfunc (c *fakeClock) Advance(d time.Duration) {\n\tc.t = c.t.Add(d)\n}\n\nfunc TestNewValidation(t *testing.T) {\n\tif _, err := New(0, 1); err == nil {\n\t\tt.Error(\"expected error for zero rate\")\n\t}\n\tif _, err := New(1, 0); err == nil {\n\t\tt.Error(\"expected error for zero burst\")\n\t}\n\tif _, err := New(-1, 1); err == nil {\n\t\tt.Error(\"expected error for negative rate\")\n\t}\n\tif _, err := NewWithClock(1, 1, nil); err == nil {\n\t\tt.Error(\"expected error for nil clock\")\n\t}\n\tif _, err := New(1, 1); err != nil {\n\t\tt.Errorf(\"unexpected error: %v\", err)\n\t}\n}\n\nfunc TestAllowN(t *testing.T) {\n\tstart := time.Date(2024, 1, 1, 0, 0, 0, 0, time.UTC)\n\n\ttests := []struct {\n\t\tname string\n\t\trate float64\n\t\tburst int\n\t\tsteps []struct {\n\t\t\tadvance time.Duration\n\t\t\tn int\n\t\t\twant bool\n\t\t}\n\t\twantTokens float64\n\t}{\n\t\t{\n\t\t\tname: \"burst exhausted immediately\",\n\t\t\trate: 1,\n\t\t\tburst: 2,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{0, 2, true},\n\t\t\t\t{0, 1, false},\n\t\t\t},\n\t\t\twantTokens: 0,\n\t\t},\n\t\t{\n\t\t\tname: \"refill over time\",\n\t\t\trate: 2, // 2 tokens/sec\n\t\t\tburst: 2,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{0, 2, true},\n\t\t\t\t{0, 1, false},\n\t\t\t\t{500 * time.Millisecond, 1, true},\n\t\t\t\t{0, 1, false},\n\t\t\t},\n\t\t\twantTokens: 0,\n\t\t},\n\t\t{\n\t\t\tname: \"tokens cap at burst\",\n\t\t\trate: 1,\n\t\t\tburst: 3,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{0, 1, true},\n\t\t\t\t{10 * time.Second, 3, true},\n\t\t\t},\n\t\t\twantTokens: 0,\n\t\t},\n\t\t{\n\t\t\tname: \"partial refill insufficient\",\n\t\t\trate: 1,\n\t\t\tburst: 1,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{0, 1, true},\n\t\t\t\t{100 * time.Millisecond, 1, false},\n\t\t\t\t{900 * time.Millisecond, 1, true},\n\t\t\t},\n\t\t\twantTokens: 0,\n\t\t},\n\t\t{\n\t\t\tname: \"multi-token request\",\n\t\t\trate: 10,\n\t\t\tburst: 5,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{0, 3, true},\n\t\t\t\t{0, 3, false},\n\t\t\t\t{0, 2, true},\n\t\t\t},\n\t\t\twantTokens: 0,\n\t\t},\n\t\t{\n\t\t\tname: \"zero and negative n rejected\",\n\t\t\trate: 1,\n\t\t\tburst: 1,\n\t\t\tsteps: []struct {\n\t\t\t\tadvance time.Duration\n\t\t\t\tn int\n\t\t\t\twant bool\n\t\t\t}{\n\t\t\t\t{0, 0, false},\n\t\t\t\t{0, -1, false},\n\t\t\t\t{0, 1, true},\n\t\t\t},\n\t\t\twantTokens: 0,\n\t\t},\n\t}\n\n\tfor _, tc := range tests {\n\t\tt.Run(tc.name, func(t *testing.T) {\n\t\t\tclk := &fakeClock{t: start}\n\t\t\tl, err := NewWithClock(tc.rate, tc.burst, clk)\n\t\t\tif err != nil {\n\t\t\t\tt.Fatalf(\"NewWithClock: %v\", err)\n\t\t\t}\n\t\t\tfor i, s := range tc.steps {\n\t\t\t\tclk.Advance(s.advance)\n\t\t\t\tif got := l.AllowN(s.n); got != s.want {\n\t\t\t\t\tt.Errorf(\"step %d: AllowN(%d) = %v, want %v\", i, s.n, got, s.want)\n\t\t\t\t}\n\t\t\t}\n\t\t\tif got := l.Tokens(); got != tc.wantTokens {\n\t\t\t\tt.Errorf(\"final tokens = %v, want %v\", got, tc.wantTokens)\n\t\t\t}\n\t\t})\n\t}\n}\n\nfunc TestConcurrentAccess(t *testing.T) {\n\tclk := &fakeClock{t: time.Now()}\n\tl, err := NewWithClock(1000, 100, clk)\n\tif err != nil {\n\t\tt.Fatalf(\"NewWithClock: %v\", err)\n\t}\n\tvar wg sync.WaitGroup\n\tvar mu sync.Mutex\n\tallowed := 0\n\tfor i := 0; i < 50; i++ {\n\t\twg.Add(1)\n\t\tgo func() {\n\t\t\tdefer wg.Done()\n\t\t\tif l.AllowN(1) {\n\t\t\t\tmu.Lock()\n\t\t\t\tallowed++\n\t\t\t\tmu.Unlock()\n\t\t\t}\n\t\t}()\n\t}\n\twg.Wait()\n\tif allowed != 100 {\n\t\tt.Errorf(\"allowed = %d, want 100 (burst)\", allowed)\n\t}\n\tif l.AllowN(1) {\n\t\tt.Error(\"expected denial after burst exhausted\")\n\t}\n}\n```", + "output_characters": 5425, + "output_sha256": "24143508cdbdc04bdc644b87333d21acd66c08ea5502c91f5fddae9fe92252b1", + "prompt_tokens": 132, + "reasoning_characters": 0, + "reasoning_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", + "response_tail_seconds": 0.052937, + "run": 3, + "stream_done_marker": true, + "stream_sha256": "24143508cdbdc04bdc644b87333d21acd66c08ea5502c91f5fddae9fe92252b1", + "time_to_first_visible_seconds": 0.81513, + "time_to_last_output_seconds": 25.851851, + "ttft_seconds": 0.81513, + "wall_seconds": 25.904788 + } + ], + "time_to_last_output_seconds": { + "maximum": 33.384, + "median": 32.3, + "minimum": 25.852, + "p90": 33.384 + }, + "ttft_seconds": { + "maximum": 0.815, + "median": 0.734, + "minimum": 0.722, + "p90": 0.815 + }, + "wall_seconds": { + "maximum": 33.439, + "median": 32.303, + "minimum": 25.905, + "p90": 33.439 + } + }, + "prose": { + "completion_gate": { + "passed": 3, + "total": 3 + }, + "decode_tokens_per_second": { + "maximum": 39.614, + "median": 38.344, + "minimum": 38.244, + "p90": 39.614 + }, + "runs": [ + { + "completion_tokens": 1002, + "completion_validation": { + "valid": true + }, + "decode_seconds": 26.105945, + "decode_tokens_per_second": 38.344, + "finish_reason": "stop", + "measured_sse_events": 1000, + "output": "**MEMO: GPU Capacity \u2014 Buy More or Use What We Have Better**\n\n**To:** Engineering Leadership\n**From:** Infrastructure Team\n**Re:** Hardware expansion decision for the inference cluster\n\n**Status and context**\n\nOur four A100 nodes are running hot. Peak-hour utilization sits around 85 percent, queue times for batch jobs have crept from minutes to hours, and interactive users are starting to notice latency in the notebook environment. The obvious question on the table is whether we should purchase two additional nodes, at roughly $180k all-in with networking and support, or invest a quarter of engineering time in squeezing more out of what we already own. I've spent the last two weeks looking at the data, and I want to lay out how I think we should reason about this before we commit either way.\n\n**What the numbers actually say**\n\nThe utilization figure is misleading on its own. Eighty-five percent sounds like we're nearly maxed out, but a breakdown by time of day tells a different story. Overnight and weekend utilization drops below 30 percent, and a meaningful share of our peak load comes from jobs that are not latency-sensitive at all \u2014 model evaluation runs, embedding backfills, fine-tuning experiments. These are jobs that could tolerate a delay of several hours without anyone noticing. Right now they compete with interactive work for the same capacity because our scheduler treats them identically. A modest investment in workload segregation, better preemption, and autoscaling our batch queue into the off-peak window would, by my estimate, free up 20 to 25 percent of peak capacity. That is the equivalent of roughly one additional node, purchased with engineer-weeks rather than capital.\n\nThere is also waste we can eliminate outright. Two teams are holding reservations on GPUs they use intermittently, and our current accounting makes it easier to squat on hardware than to release and re-request it. Fixing the incentive structure \u2014 showback rather than soft reservations, with a fast path for reclaiming idle capacity \u2014 costs almost nothing and recovers real headroom.\n\n**The case for buying anyway**\n\nThat said, I don't want to oversell optimization. Some of our peak demand is genuinely inelastic: the customer-facing inference service has a hard latency budget, and it degrades visibly when utilization crosses about 90 percent. If our growth forecast is even roughly right, optimization buys us perhaps two quarters of runway, not a permanent reprieve. There's also a procurement reality to face: lead times on these nodes are currently eight to ten weeks. If we wait until the pain is undeniable, we will spend a painful quarter queuing behind our own decision. Ordering hardware is slow to undo but also slow to arrive; the option has a long fuse.\n\n**Cost, risk, and reversibility**\n\nThe capital cost is real but not existential for us this year. The subtler cost is opportunity: $180k is also two engineers for a year, or the difference between hiring and not hiring on the product side. And hardware we buy is hardware we must power, cool, and eventually depreciate \u2014 a commitment that outlives the decision that created it. Optimization, by contrast, is largely reversible. If we restructure the scheduler and it turns out demand was higher than we thought, we've lost a few engineer-weeks and learned something precise about our true ceiling. If we buy nodes and demand turns out softer than forecast, we're carrying idle capital with no easy exit.\n\nReliability cuts both ways. More nodes means more redundancy and less exposure to a single failure taking down a class of jobs. But it also means more surface area to maintain, and our current operational load is already straining one part-time admin. Better utilization of a smaller fleet, with cleaner scheduling, is arguably the more reliable posture \u2014 provided we don't run so lean that a single node failure creates an outage rather than an inconvenience.\n\n**Recommendation**\n\nDo both, in sequence, with the order mattering. Start the optimization work now: scheduler improvements, reservation reform, and off-peak batch shifting. Give it six weeks and a hard success metric \u2014 peak utilization sustained below 75 percent with interactive latency within budget. Simultaneously, place a conditional purchase order for one node, not two, with delivery timed to land just after the optimization work concludes. If optimization delivers as expected, we cancel or defer the order and bank the savings. If it falls short, the hardware arrives before the situation becomes an incident.\n\nThe honest uncertainty here is demand itself. Our forecast has been wrong in both directions before, and the cheapest hedge is to keep our options open as long as possible. Sequencing a reversible intervention ahead of an irreversible purchase does exactly that. I'd like a decision by end of week so the purchase-order window doesn't slip.\n\nHappy to walk through the utilization data with anyone who wants the detail.", + "output_characters": 4993, + "output_sha256": "8541ed6e65bb98ccd9284e992c9f3ccec68163570b3db194b33ad18edd77c24e", + "prompt_tokens": 125, + "reasoning_characters": 67, + "reasoning_sha256": "0e329108922dfc1942b39bfd181fcdd024d698835cac088b9e4fca144d040576", + "response_tail_seconds": 0.003027, + "run": 1, + "stream_done_marker": true, + "stream_sha256": "c786ebb457fa5c963f00b27a4be89bc418929b31624cab0cb78fc5153056078d", + "time_to_first_visible_seconds": 1.236199, + "time_to_last_output_seconds": 26.849068, + "ttft_seconds": 0.743124, + "wall_seconds": 26.852095 + }, + { + "completion_tokens": 1021, + "completion_validation": { + "valid": true + }, + "decode_seconds": 25.748572, + "decode_tokens_per_second": 39.614, + "finish_reason": "stop", + "measured_sse_events": 1019, + "output": "**MEMO: GPU Capacity \u2014 Buy More or Use What We Have Better**\n\n**To:** Engineering Team\n**From:** Infrastructure Working Group\n**Re:** Hardware expansion vs. utilisation improvement for our local inference fleet\n\n**Status: Recommendation to pursue utilisation improvements before purchasing additional hardware**\n\nWe've reached the point where our local AI hardware feels tight. Queue times during peak hours have crept up, a couple of recent projects were delayed waiting for GPU availability, and there's a natural temptation to solve this the straightforward way: buy more cards. I've spent the last two weeks looking at the numbers, talking to the people actually waiting on capacity, and stress-testing the alternative. My recommendation is that we invest the next quarter in improving utilisation of the hardware we already own, and defer the purchase decision until we have better evidence about what we actually need.\n\nThe cost argument is the most obvious place to start. Our current fleet is running at roughly 40\u201345% average utilisation, but that average hides a bimodal pattern: near-saturation during working hours and near-idle overnight and on weekends. Buying hardware to cover the peaks means paying for capacity that sits unused most of the time \u2014 not just the capital cost of the cards themselves, but the rack power, cooling, and the ongoing operational attention they demand. At current prices, a meaningful expansion would consume most of our discretionary budget for the year. The utilisation work, by contrast, is mostly engineering time we'd spend anyway: better job scheduling, batching small requests, quantising models where quality trade-offs are acceptable, and moving long-running batch work into off-peak windows. If we can lift average utilisation to 65\u201370%, we effectively add half a fleet's worth of capacity for the cost of a few engineer-months.\n\nLatency is the counterargument, and it deserves to be taken seriously. Some of our interactive workloads are genuinely latency-sensitive, and aggressive scheduling or batching can add tail latency if done carelessly. But the current pain isn't uniform. When I traced the recent delays, most of the queueing came from batch jobs colliding with interactive traffic, not from an absolute shortage of compute. Separating those workloads \u2014 reserving a slice of capacity for interactive use and pushing batch work to off-peak \u2014 would relieve most of the observed latency without touching anyone's interactive experience. Where we do find genuine latency pressure after that separation, we'll know it's real rather than an artefact of poor scheduling, and that's exactly the evidence we'd need to size a purchase correctly.\n\nReliability cuts both ways, and I want to be honest about that. Higher utilisation means less headroom. If a node fails while we're running at 70%, the impact is worse than it would be at 40%. Our current fleet has no meaningful redundancy for our two most critical workloads, and that's a real risk regardless of what we decide. My view is that the utilisation project should explicitly include a reliability review: identify which workloads are genuinely critical, ensure they have failover capacity, and accept lower utilisation targets on the hardware that backs them. Buying more hardware without doing this work would just give us more machines to fail simultaneously; the reliability problem is orthogonal to capacity, and we should stop letting it masquerade as one.\n\nThe opportunity cost question is really about what we'd give up. If we spend the budget on hardware now, we can't spend it on the model efficiency work, the evaluation pipeline, or the two product initiatives currently competing for the same funds. If we spend the time on utilisation, we defer capacity expansion by a quarter or two, and there's some risk that a project lands in that window and finds itself waiting. I judge that risk acceptable \u2014 the recent delays were measured in days, not weeks, and the scheduling changes should absorb most of that variance.\n\nFinally, reversibility. Utilisation improvements are almost entirely reversible: scheduling policies can be changed, batching can be tuned, and nothing we do locks us in. A hardware purchase is largely irreversible \u2014 the money is spent, the depreciation clock starts, and if our workload mix shifts (as it has twice in the last year), we may find ourselves holding the wrong kind of capacity. Deferring the purchase preserves optionality, and optionality is worth a lot when our demand curve is this uncertain.\n\nTo be clear about uncertainty: my utilisation figures come from coarse instrumentation, and it's possible that deeper measurement reveals a harder capacity ceiling than I expect. If, after the scheduling and batching work, we're still saturating at 70% with real unmet demand, we should buy \u2014 and we'll do so with much better data about what to buy. I propose we revisit this decision at the end of next quarter with measured numbers in hand.\n\nHappy to walk through the analysis in detail at Thursday's sync.", + "output_characters": 5064, + "output_sha256": "5064ee97b27d72d60eb1637cdbc5903c68ec2d2882989900c092f16aedff9fdd", + "prompt_tokens": 121, + "reasoning_characters": 115, + "reasoning_sha256": "835c54cfa519e7b718758bd2a6a7b3d9e7a1cb4031b1181cf36b5c7862b9f24d", + "response_tail_seconds": 0.002486, + "run": 2, + "stream_done_marker": true, + "stream_sha256": "5c923228aaaac655533907c98651bd7624653ffa370563d32380aa02754b2b7b", + "time_to_first_visible_seconds": 1.362454, + "time_to_last_output_seconds": 26.476008, + "ttft_seconds": 0.727435, + "wall_seconds": 26.478493 + }, + { + "completion_tokens": 969, + "completion_validation": { + "valid": true + }, + "decode_seconds": 25.311028, + "decode_tokens_per_second": 38.244, + "finish_reason": "stop", + "measured_sse_events": 967, + "output": "**MEMO: GPU Capacity \u2014 Buy More or Use What We Have Better**\n\n**To:** Engineering Leadership\n**From:** Infrastructure Team\n**Re:** Recommendation on AI hardware capacity for the next two quarters\n\nWe've reached the point where our inference and training workloads are bumping against the ceiling of the hardware we own. The question on the table is whether we should purchase additional accelerators or invest in raising the utilisation of the fleet we already have. After several weeks of measurement and discussion, my recommendation is that we spend the next quarter on utilisation, with a small, pre-approved budget line for targeted hardware purchases only if utilisation work stalls. Here is the reasoning.\n\nThe case for buying more hardware is straightforward: our queue times during peak hours are real, they hurt iteration velocity, and the fastest way to make them disappear is more capacity. But the purchase path carries costs that are easy to underestimate. Capital expenditure on accelerators is substantial, and the depreciation curve is unforgiving \u2014 hardware that looks reasonably priced today may look poor value in eighteen months as the vendor cycle moves. There is also an operational cost we tend to ignore: every additional node is something we must power, cool, monitor, and eventually decommission. Our facilities headroom is not unlimited, and a purchase decision made now constrains rack space and power budget for years.\n\nMore importantly, buying hardware is largely irreversible at our scale. Reselling used accelerators recovers only a fraction of cost, and the procurement lead times mean that a wrong-sized purchase cannot be quickly corrected. Utilisation improvements, by contrast, are highly reversible. Better batching, smarter scheduling, mixed-precision inference where quality permits, and killing zombie notebooks that squat on memory are all changes we can make, measure, and undo if they degrade service. That asymmetry in reversibility alone tilts the decision, but it is reinforced by the numbers: our fleet currently sits at roughly 40% average utilisation, with peak-hour contention driven less by aggregate demand than by poor scheduling. If we can push sustained utilisation to 65\u201370%, we effectively add half a fleet's worth of capacity at a fraction of the cost of buying it.\n\nLatency is the strongest counterargument, and I want to treat it honestly. Some of our latency-sensitive serving paths are already near their limits, and squeezing more throughput onto them risks p95 regressions that would be visible to users. The utilisation work must therefore be scoped carefully: it should target batch workloads, development and experimentation jobs, and off-peak scheduling \u2014 not the interactive serving tier. For the serving tier, if measurement shows genuine headroom constraints, a modest purchase of one or two nodes dedicated to latency-critical inference is defensible and cheap relative to a fleet-wide expansion. That is the targeted purchase I'd pre-approve, contingent on the utilisation work not freeing enough capacity by mid-quarter.\n\nReliability cuts both ways. A larger fleet means more components and more failure surface, but it also means more redundancy: with more nodes, losing one to a driver crash or a failing fan is an inconvenience rather than an outage. Today, a single hardware failure during a training run can cost us days. I don't want to dismiss that. However, the same reliability benefit can be had more cheaply through checkpointing discipline and job-level fault tolerance, which are software problems we control, rather than through capital we must commit. If our training jobs were resumable within minutes, the redundancy argument for buying hardware weakens considerably.\n\nThe opportunity cost deserves explicit mention. Every dollar spent on accelerators this quarter is a dollar not spent on the evaluation pipeline, the observability work, and the model-quality projects that several teams are waiting on. Hardware is visible and satisfying; those projects are less glamorous but compound. Conversely, deferring a purchase has its own opportunity cost if hiring plans bring new workloads online faster than expected. That is the main uncertainty in my recommendation: our demand forecast for the second half of the year is soft, and if the new fine-tuning programme lands on schedule, utilisation gains may be absorbed within a quarter. I accept that risk. The cost of being wrong is a delayed purchase, which is recoverable; the cost of buying prematurely is sunk capital and crowded racks, which is not.\n\nIn summary: commit the next quarter to utilisation engineering with clear targets, hold a small reserved budget for latency-critical nodes if serving headroom proves genuinely constrained, and revisit the fleet-expansion question in Q3 with better demand data and a fleet that is working harder for us. 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