Frank
8212d4451a
Mechanical identity rename across the knowledge base, AGENTS.md, README, the
hermes config example, and homepage services.json. No behavior change; served
aliases on the LLM endpoints change with the deploy of commit 69f87c8.
94 lines
3.6 KiB
Markdown
94 lines
3.6 KiB
Markdown
---
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type: research
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title: FreeToken Benchmark Sweep — Dankserver RTX 3090
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description: KV pool vs decode speed sweep across bs1/bs3/bs4/bs6 on Dankserver with Qwen3.6-35B-A3B-FP8 via FreeToken v0.1.2.
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tags:
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- freetoken
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- benchmark
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- dankserver
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- frank-small
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- qwen3.6
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timestamp: 2026-09-02
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---
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# FreeToken Benchmark Sweep — Dankserver RTX 3090
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Full sweep of KV pool size vs decode speed at batch sizes 1, 3, 4, and 6.
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Measured 2026-09-02 with FreeToken v0.1.2 serving `Qwen/Qwen3.6-35B-A3B-FP8`
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on Dankserver (RTX 3090 24 GB, 60 GB RAM, driver 595.84).
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## Methodology
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- Each point: restart at low KV (8k), live-rebuild upward via
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`ft ctl cache --kv N --moe M` (auto-computed MoE slot count).
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- Warmup: 2–3 iterations at 64 tokens (fills LRU expert cache).
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- Measured: 2–3 iterations at 128 tokens, `temperature=0`, `ignore_eos=true`.
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- Prompt: "Count from 1 to 100." (short — isolates decode bandwidth).
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- Per-token KV cost: ~20 KiB (10 full-attention layers × 2 KV heads ×
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256 dim × bf16). Per-slot MoE cost: ~3 MiB.
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- GPU budget: ~17.6 GiB after weights + fixed caches.
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## bs1 vs bs3 vs bs4 vs bs6
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| KV pool | Residency | bs1 | bs3 agg | bs3/req | bs4 agg | bs4/req | bs6 agg | bs6/req |
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|---------|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
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| 8,192 | ~89% | 56.7 | 108.8 | 36.3 | 150.6 | 37.7 | 132.1 | 22.0 |
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| 65,536 | ~83% | ~52 | 106.1 | 35.4 | 148.0 | 37.0 | 131.8 | 22.0 |
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| 131,072 | ~76% | ~48 | 99.9 | 33.3 | 144.4 | 36.1 | 131.8 | 22.0 |
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| 262,144 | ~62% | ~48 | 85.3 | 28.4 | 131.3 | 32.8 | 130.5 | 21.8 |
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| 400,000 | ~47% | ~42 | 73.3 | 24.4 | 116.5 | 29.1 | 161.1 | 26.9 |
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| 524,288 | ~30% | 34.8 | 59.8 | 19.9 | 102.2 | 25.5 | 150.6 | 25.1 |
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| 655,360 | ~19% | 30.4 | 42.8 | 14.3 | 86.9 | 21.7 | 131.5 | 21.9 |
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| 700,000 | ~15% | 28.3 | 38.5 | 12.8 | 83.0 | 20.8 | 121.1 | 20.2 |
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## Key Findings
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### No cliff — gentle slope
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The LRU expert cache absorbs residency reductions gracefully. Even at
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5% residency (544 slots), bs1 decode is 25.2 tok/s — 44% of baseline.
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### Free zone up to ~262k
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Decode speed is flat from 8k through 262k at bs1 and bs4. Giving up
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expert residency (89% → 62%) costs nothing because the LRU working set
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fits in fewer slots.
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### bs4 is the sweet spot
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bs4 dominates at every high-residency operating point. Per-request
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speed at bs4 (37.7 tok/s at 8k) is *faster* than bs3 (36.3) because 4
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concurrent requests better fill the PCIe expert-streaming pipeline.
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### bs6 crossover at ~400k
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At high residency, bs6 aggregate (132) < bs4 aggregate (150) — the
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bottleneck is GPU compute on attention layers, not PCIe. At low
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residency (400k+), the bottleneck flips to PCIe expert streaming and
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bs6 wins (161 vs 116 at 400k) because more concurrency fills the
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pipeline.
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### bs3 is strictly worse than bs4
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Fewer concurrent users leave PCIe idle gaps. At every point past 8k,
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bs3 per-request is slower than bs4 per-request. Don't run bs3 — set
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`--max-running-requests 4` regardless of actual user count.
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### 800k doesn't fit
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4 × 200k = 800k exceeds the GPU budget even at minimum MoE (256 slots).
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The absolute ceiling is ~750k tokens. With radix prefix sharing,
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effective capacity exceeds naive `pool ÷ concurrency`.
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## Comparison: old llama.cpp service
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The old Tiel-Coder llama.cpp service ran 200,700 total context across
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3 parallel slots (66,900/slot) at ~99 tok/s aggregate at concurrency 4.
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FreeToken at 131k KV (bs4) does 144 tok/s aggregate — 45% faster — with
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the caveat that it serves base Qwen3.6-35B-A3B weights, not the
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Tiel-Coder finetune.
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## See Also
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- [[freetoken-frank-small]] — production runbook
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- [[dealign-ornith-crack]] — the prior llama.cpp deployment (retired) |