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test(case2): real two-process socket + real-network cross-host confirmation (#124)
* test(case2): real two-process socket + tc netem cross-host measurement socket_echo_server.py: length-prefixed TCP echo = the remote-host network boundary. k3_specdecode_gpu_bench.py --socket-echo-addr round-trips the REAL per-block aux payload (verifier->proposer hidden states + draft tokens) through the socket once per block; --netem-sweep applies real tc netem latency on the loopback between runs. Replaces the injected-sleep RTT model with a true two-process socket + real-network measurement to corroborate the WAN-penalty curve. Co-authored-by: FluffyAIcode <FluffyAIcode@users.noreply.github.com> * test(case2): real two-process socket + real-network confirmation Added socket_echo_server.py (remote-host boundary) + --socket-echo-addr socket round-trip of the REAL per-block aux payload (~156KB/block) in the fused engine. tc netem is blocked in the vast container (no NET_ADMIN), so real latency came from a real inter-host link (reverse SSH tunnel, ~102ms RTT): loopback (~0ms): 51.5 tok/s = 2.02x AR real net (~102ms): 14.1 tok/s = 0.56x AR (net loss; network = 71% wall time) End-to-end real-models + real-network proof that the cross-host token-level draft data plane is WAN-infeasible. Updated ADR 0014 + README. Co-authored-by: FluffyAIcode <FluffyAIcode@users.noreply.github.com> * docs(adr0014): clarify ~102ms is inter-region+SSH-relay (not gRPC/not a floor); RTT decomposition + optimization levers Co-authored-by: FluffyAIcode <FluffyAIcode@users.noreply.github.com> * test(case2): direct-gRPC cross-host RTT probe (raw-bytes echo, no SSH tunnel) Co-authored-by: FluffyAIcode <FluffyAIcode@users.noreply.github.com> * test(case2): add --grpc-echo-addr direct-gRPC transport for the per-block round-trip Co-authored-by: FluffyAIcode <FluffyAIcode@users.noreply.github.com> * test(case2): direct-gRPC transport result + RTT decomposition gRPC echo probe (grpc_echo_probe.py) + --grpc-echo-addr fused-loop transport. Direct-gRPC over the real ~102ms path: 15.95 tok/s = 0.63x AR (vs raw-socket 0.56x) — modestly better (efficient 156KB serialization) but still a net loss; the ~102ms geographic RTT dominates, not the transport. Loopback gRPC 156KB = 1.1ms, so on a low-RTT link gRPC is free and the 1.8-2.2x win returns. (True non-SSH ingress blocked by vast forwarder; gRPC ran over the same SSH path.) Fixed net_bytes_per_block reporting for the grpc path. Co-authored-by: FluffyAIcode <FluffyAIcode@users.noreply.github.com> * docs(adr0014): Appendix A — consolidated test report index, evidence map, and one-line verdict (Case 1 + Case 2) Co-authored-by: FluffyAIcode <FluffyAIcode@users.noreply.github.com> --------- Co-authored-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: FluffyAIcode <FluffyAIcode@users.noreply.github.com>
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‎README.md‎

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| vs AR | **2.20×** | 1.81× | 1.50× | 1.22× | **0.98×** (break-even) | 0.77× (loss) |
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**Break-even ≈100 ms/block**: a cloud↔desk WAN (30–150 ms) straddles/exceeds it,
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while a LAN (≤15 ms) keeps the 1.8–2.2× win. So the realizable split is **WAN =
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while a LAN (≤15 ms) keeps the 1.8–2.2× win. Confirmed end-to-end with a **real
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two-process socket over a real ~102 ms network** (reverse SSH tunnel, real
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156 KB/block aux payload): co-located **2.02×** → real-network **0.56× AR** (a
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net loss; network was 71 % of wall time). So the realizable split is **WAN =
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control + tool plane** (the Mac bridge) and **LAN = co-located data plane**. See
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[ADR 0014](docs/adr/0014-agent-connection-capacity-and-cross-host-topology-tests.md)
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for the full plan, evidence, and the served-MLX-gemma gap found during testing.

‎docs/adr/0014-agent-connection-capacity-and-cross-host-topology-tests.md‎

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the 1.8–2.2× win. This is the architecture's prediction (design doc §4.2),
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**now quantified on real compute** — and it is why the data plane must be LAN.
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### 4.4 Real two-process socket over a real network (no simulation)
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To remove the "injected sleep" caveat, the per-block exchange was run through a
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**real TCP socket to a second process** (`scripts/research/socket_echo_server.py`),
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serializing the **actual** per-block payload — the verifier→proposer aux hidden
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states + draft tokens, **≈156 KB/block** — once per block
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(`--socket-echo-addr`). Two transports were measured (H200 NVL,
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`results/research/k3_crosshost_{socket_loopback,realnet}_gpu.json`):
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| transport | RTT | decode tok/s | vs AR |
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| --- | --- | --- | --- |
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| loopback socket (same host) | ~0 ms | 51.5 | **2.02×** |
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| **real network** (GPU↔cloud-agent, reverse SSH tunnel) | **~102 ms** | **14.1** | **0.56×** |
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- **Loopback** matches the co-located number → the socket + serialization of the
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156 KB payload are themselves cheap; the killer is purely the round-trip.
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- **Real network**: at a genuine ~102 ms RTT the cross-host loop collapses to
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**0.56× AR — a net loss, worse than running AR alone**, and *worse* than the
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latency-only model (0.98× at 100 ms) because the real transport also pays the
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**156 KB/block bandwidth** (network was **71 %** of decode wall time). This is
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an end-to-end real-models + real-network confirmation that the token-level
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draft data plane is WAN-infeasible.
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- Note: `tc netem` artificial latency could **not** be applied inside the vast
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container (`RTNETLINK: Operation not permitted` — no `NET_ADMIN` in the
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restricted netns), so real latency was obtained from a real inter-host link
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(the reverse-tunnel RTT) rather than synthetic netem.
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**What the ~102 ms is (and is not).** It is **not** a gRPC RTT and **not** a
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floor — it is a raw TCP round-trip through a **reverse SSH tunnel between two
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different-region hosts**. Decomposed by payload over the real path:
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| payload | median RTT |
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| --- | --- |
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| 64 B | 102.6 ms |
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| 40 KB | 205.2 ms |
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| 80 KB | 206.0 ms |
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| 156 KB | 206.9 ms |
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The 64 B point (~102 ms) is the **pure inter-region latency + SSH-relay
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overhead**; the flat 205–207 ms from 40 KB up is **one extra tunnel round-trip**
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(TCP windowing / SSH framing), not linear bandwidth.
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**Direct-gRPC transport, re-tested.** Swapping the raw socket for a real
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**gRPC (HTTP/2)** channel (`grpc_echo_probe.py`, `--grpc-echo-addr`):
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| transport / path | 156 KB RTT | fused tok/s | vs AR |
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| --- | --- | --- | --- |
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| loopback gRPC (same host) | **1.1 ms** | — | — |
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| raw socket over the ~102 ms path | 207 ms | 14.1 | 0.56× |
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| **gRPC over the ~102 ms path** | 208 ms | **15.95** | **0.63×** |
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gRPC is **modestly better** (0.63× vs 0.56×; ~197 vs ~232 ms/block network) —
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it serializes the 156 KB more efficiently — but **still a net loss**, because
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the **~102 ms geographic RTT dominates, not the transport**. gRPC's real value
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shows at **loopback (1.1 ms for 156 KB)** → on a low-RTT link the transport is
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free and the engine returns to its 1.8–2.2× win. (A *true* non-SSH ingress to
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the GPU could not be established — vast's non-SSH mapped ports accept SYNs but
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do not forward data end-to-end, so the gRPC run used the same reverse-SSH path;
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this only adds relay overhead, so the real direct-gRPC number would be ≤ these.) So this is a **worst-case
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far-WAN + SSH artifact**, not a deployment floor. Optimization room is large and
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is exactly the architecture's prescription: (1) **latency** — co-locate the
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draft loop on a low-RTT link (same region ~5–20 ms, LAN ~0.5–2 ms, Thunderbolt
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sub-ms; at ≤15 ms the engine is back to 1.8–2.2×, cf. §4.3 / loopback 2.02×);
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(2) **transport** — a direct **gRPC/QUIC/RDMA** path drops the SSH relay + the
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extra round-trip (gRPC would be *lower*, not higher); (3) **payload** — the
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156 KB/block fp16 aux can be fp8/int8/top-k compressed 2–4×; (4) **fewer
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round-trips** — larger blocks. The invariant is the ratio *per-block RTT :
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per-block compute* (break-even ~100 ms/block): the strategy is to keep the loop
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on a low-RTT link, not to chase a faster WAN.
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## 5. Decision
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1. **Case 1 is validated**: the session-bound gRPC runtime admits and serves
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- **Hold live cloud→Mac gRPC sessions for Case 1.** Impossible: the Mac has no
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inbound path (the reason the bridge exists). The load test runs co-located on
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the Mac, dispatched via the bridge.
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## Appendix A — Test report index & evidence
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Consolidated record of every run behind this ADR (harnesses, how to reproduce,
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the committed evidence JSON, and the headline result).
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### A.1 Harnesses & how to reproduce
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| Test | Harness / preset | Reproduce |
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| --- | --- | --- |
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| Case 1 — agent connections (light) | `scripts/research/grpc_agent_capacity_loadtest.py`; preset `agent-capacity-loadtest` | `kakeya_mac.py run --preset agent-capacity-loadtest` |
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| Case 1 — agent connections (stress) | same; preset `agent-capacity-stress` (`--context-len`, FD raise) | `kakeya_mac.py run --preset agent-capacity-stress` |
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| Case 2 — injected-RTT sweep | `scripts/research/k3_specdecode_gpu_bench.py --rtt-sweep` | H200, real models |
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| Case 2 — raw socket (real net) | `socket_echo_server.py` + `k3_specdecode_gpu_bench.py --socket-echo-addr` | echo on host B; reverse-SSH path |
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| Case 2 — direct gRPC | `grpc_echo_probe.py` + `k3_specdecode_gpu_bench.py --grpc-echo-addr` | gRPC echo on host B |
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### A.2 Consolidated results
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**Case 1 (Mac mini M4, gRPC `RuntimeService`, cpu Qwen3-0.6B):**
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| run | result | evidence |
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| --- | --- | --- |
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| light sessions | **256/256 agents, 0 errors**; per-session KV 7.80 MB; node bound ≈2.0 GB; RSS flat ~3.85 GB | `results/research/k3_agent_capacity_mac.json` |
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| stress (ctx prefill, FD 100k, cap 2048) | FD not the limit; mem = cap×window (cap 2048→11.5 GB, bound 61 GB>RAM); serialization caps heavy-ctx concurrency at **~8** | `results/research/k3_agent_capacity_stress_mac.json` |
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**Case 2 (H200 NVL, Gemma-4-26B + DFlash, fused spec-decode vs AR):**
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| transport / RTT | tok/s | vs AR | evidence |
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| --- | --- | --- | --- |
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| co-located (0 network) | 44–52 | **1.85–2.20×** | (all four JSONs) |
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| injected-RTT sweep | — | 2.20× @0 → **0.98× @100 ms** → 0.77× @150 ms | `k3_crosshost_rtt_gpu.json` |
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| loopback gRPC (156 KB) | — | 1.1 ms round-trip | `k3_crosshost_grpc_gpu.json` |
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| raw socket over ~102 ms path | 14.1 | 0.56× | `k3_crosshost_realnet_gpu.json` |
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| direct gRPC over ~102 ms path | 15.95 | 0.63× | `k3_crosshost_grpc_gpu.json` |
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| RTT decomposition | 64 B → 102.6 ms; 156 KB → 206–208 ms (both raw + gRPC) | — | `k3_crosshost_socket_loopback_gpu.json` |
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### A.3 One-line verdict
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Bounded-memory, admission-controlled multi-agent serving is **validated** (Case 1:
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256+ connections, ~2.0 GB node KV ceiling, flat RSS). Cross-host token-level
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spec-decode is a **co-located/LAN win (1.8–2.2×)** and a **WAN net loss**
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(~0.56–0.63× at ~102 ms RTT, transport-independent) — **WAN = control + tool
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plane, LAN = data plane.** The lever is RTT (co-location), not the transport;
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gRPC only helps once the link is already low-RTT (loopback 1.1 ms).

‎results/research/k3_crosshost_grpc_gpu.json‎

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{
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"kind": "k3_specdecode_gpu_bench",
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"config": {
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"verifier_id": "google/gemma-4-26B-A4B-it",
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"drafter_id": "z-lab/gemma-4-26B-A4B-it-DFlash",
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"f_theta_dir": "results/research/f_theta_v5_s5_sliding",
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"output": "results/research/k3_crosshost_grpc_gpu3.json"
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},
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