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test: multi-tenant resident-window pressure A/B vs MLX-native (ADR 0014 §3.4) (#126)
* docs: spell out 'FD' as open-file-descriptor limit (RLIMIT_NOFILE) in ADR 0014 + README Co-authored-by: FluffyAIcode <FluffyAIcode@users.noreply.github.com> * test: multi-tenant resident-window pressure harness + A/B vs MLX-native scripts/research/mlx_multitenant_pressure.py: per-agent KV + max concurrent agents in a memory budget, Kakeya S5 (sink+window) vs gemma native hybrid cache (sliding bounded to 1024). Ramps agents via cache replication (real N x memory), measures peak mem + per-agent decode tok/s. Addresses the single-tenant pressure gap (true multi-tenant served path = PR-A3c; this measures the model/cache-level capacity that is the real differentiator). New preset mlx-multitenant-pressure. Co-authored-by: FluffyAIcode <FluffyAIcode@users.noreply.github.com> * test(multitenant): real per-agent prefills (drop COW clone), add S5 recall-preserving config + sinkwin floor; A/B uses derived max-agents from measured per-agent KV Co-authored-by: FluffyAIcode <FluffyAIcode@users.noreply.github.com> * docs(adr0014): §3.4 multi-tenant resident-window pressure test — A/B vs MLX-native Real per-agent prefills (not COW): Kakeya S5 fits ~4.2x more concurrent agents than gemma-native at ctx2048 (per-agent KV 61.1 vs 256.9 MB; budget hit 32 vs 15; derived 93 vs 22), recall-preserving. Pure sink+window floor 16.8x but sacrifices recall. Addresses the single-tenant pressure-test gap; true parallel served path needs PR-A3c. Evidence + Appendix A row added. 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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‎docs/adr/0014-agent-connection-capacity-and-cross-host-topology-tests.md‎

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(`SessionStore`) + slab pool (`PoolExhausted → RESOURCE_EXHAUSTED`) are the
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admission-control levers; `--max-concurrent-rpcs` caps in-flight handlers.
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### 3.4 Multi-tenant resident-window pressure test — A/B vs MLX-native
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§3.2/§3.3 measure *connection admission* on the single-tenant served path. The
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question that actually matters for many concurrent agents is **how many agents,
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each with its own resident KV window, fit in a memory budget** — the
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multi-tenant *capacity*, and the axis where a bounded window should win. The
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served path can't answer this (per-session binding is PR-A3c), so it is measured
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at the **model/cache level**: build one independent KV cache per agent, prefill
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each to a context length, and ramp the agent count with **real per-agent
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prefills** (real N× memory) until a memory budget is hit
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(`scripts/research/mlx_multitenant_pressure.py`, preset
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`mlx-multitenant-pressure`; `results/research/k3_multitenant_pressure_mac.json`).
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Result (Mac mini M4, gemma-4-26B-A4B 4-bit, **ctx 2048**, 21 GB budget):
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| config | per-agent KV | budget hit at | derived max agents (KV budget) |
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| --- | --- | --- | --- |
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| MLX-native (gemma hybrid cache) | **256.9 MB** | N=15 | ~22 |
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| **Kakeya S5** (recall-preserving) | **61.1 MB** | N=32 | ~93 |
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| Kakeya pure sink+window (no recall) | 15.3 MB | — | ~370 |
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- **Kakeya S5 fits ~4.2× more concurrent agents** than MLX-native at equal
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context, **with recall preserved** (the 5 full-attention layers stay exact;
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only the 25 sliding layers drop from gemma's native 1024-window to
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`sink+window`=68). The measured budget-hit points (15 vs 32) confirm the
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per-agent-KV ratio empirically.
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- Honest nuance: gemma's *native* cache **already** bounds sliding layers to
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1024, so the win vs native is **4.2×**, not the headline 16.8× one gets vs a
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pure sink+window cache — but pure sink+window **sacrifices long-context
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recall**, so S5 is the fair recall-preserving comparison. The ratio shrinks at
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longer context (the shared 5 full-attention layers grow with ctx in both).
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- This is **memory-fit capacity**, not parallel-inference throughput: a single
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Mac GPU serializes/batches compute, so per-agent decode rate is unchanged; the
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multi-tenant value is fitting **~4× more bounded-window agents** in the same
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RAM. A truly parallel served path still needs PR-A3c (§6).
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## 4. Case 2 — cross-host proposer/verifier (FEASIBILITY VERDICT)
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### 4.1 Verdict: the requested topology is not implementable today, and is architecturally bounded out
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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, file-descriptor limit 100k, cap 2048) | open-file-descriptor limit not the constraint; 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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| multi-tenant capacity A/B (ctx2048, model-level) | per-agent KV native 256.9 MB vs **S5 61.1 MB**; **~4.2× more agents** (budget hit 15 vs 32; derived 22 vs 93) — recall-preserving | `results/research/k3_multitenant_pressure_mac.json` |
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**Case 2 (H200 NVL, Gemma-4-26B + DFlash, fused spec-decode vs AR):**
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‎inference_engine/bridge/manifest.py‎

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timeout_minutes=90,
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validate_reports=False,
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),
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Preset(
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name="mlx-multitenant-pressure",
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description="Multi-tenant resident-window pressure test + A/B vs "
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"MLX-native: per-agent KV and max concurrent agents in "
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"a memory budget, Kakeya S5 sink+window vs gemma's "
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"native hybrid cache, on the real MLX gemma verifier.",
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command_templates=(
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(
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"python3", "scripts/research/mlx_multitenant_pressure.py",
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"--verifier-path", "${ENV:KAKEYA_MAC_VERIFIER_PATH}",
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"--mode", "both",
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"--context-len", "2048",
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"--sink", "4", "--window", "64",
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"--max-agents", "64",
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"--mem-budget-mb", "21000",
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"--decode-steps", "16",
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"--output",
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"results/research/k3_mac_bridge_multitenant_pressure.json",
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),
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),
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timeout_minutes=120,
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validate_reports=False,
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),
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Preset(
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name="agent-capacity-stress",
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description="Test case 1 (stress): push concurrent agents to 2048 "

‎results/research/k3_multitenant_pressure_mac.json‎

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{
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"kind": "mlx_multitenant_pressure",
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"schema_version": 1,
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"config": {
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"context_len": 2048,
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"sink": 4,
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"window": 64,
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],
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"decode_steps": 16
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},
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"results": {
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"native": {
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"mode": "native",
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"per_agent_kv_mb": 256.9,
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"max_agents_measured": 15,
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"max_agents_hit_budget": true,
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"derived_max_agents_in_kv_budget": 22,
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"kv_budget_mb": 5741.4,
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"prefill_s": 12.65,
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"decode_tokens_per_s_per_agent": 22.293,
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"derived_max_agents_in_kv_budget": 93,
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"decode_tokens_per_s_per_agent": 23.027,
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}
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}
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}

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