Gahow Wang
ad754cfe0b
Sweeps GPU KV-cache capacity (--num-gpu-blocks-override) under a closed-loop replay (concurrency 4) of a controlled multi-turn workload (cumulative intra-session prefix, gen_synth_trace.py), measuring realized APC (prefix_cache hits/queries delta) and latency per capacity. Result: a sharp knee at 3.6 GB = exactly the active working set (4 sessions x 0.91 GB). APC rises 7->12->36->80% then saturates at the ~71% intra-session ceiling; TTFT p90 collapses 13.0 s -> 0.53 s at the same point; dead flat to 14.5 GB, 100% completion throughout. So only the active working set needs HBM; capacity beyond it -- and the CPU/storage tier built to chase the reuse tail -- buys ~0. Knee scales linearly with concurrency = cluster GPU count. README.md ties exp(a)+exp(b) into the section-2.2 GPU-hit-first argument with tables, conclusions, and caveats. Raw per-request dumps gitignored; summary/m0/m1 deltas kept. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
100 lines
5.3 KiB
Markdown
100 lines
5.3 KiB
Markdown
# v2 — Evidence for the GPU-hit-first principle (§2.2)
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Two experiments that turn "**Hits on GPU > hits on CPU**" + "**GPU is enough to
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hold most of the *valuable* KV reuse**" from assertion into measurement.
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Hardware: dash0, 1× NVIDIA H20 (97 GB) per experiment, Qwen3-Coder-30B-A3B-Instruct,
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vLLM 0.18.1 (V1, prefix caching, enforce-eager). KV = 96 KiB/token (1 GiB = 10,923 tok).
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## Exp (a) — three-tier hit latency (`exp_a_tier_latency/`)
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TTFT of serving a reused prefix of length L from each tier:
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- **miss** — fresh unique prompt → full prefill (recompute)
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- **GPU hit** — re-request → HBM prefix cache
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- **CPU hit** — warm → evict to CPU offload tier (`--kv-offloading-size`) → re-request → DRAM fetch
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- **PCIe floor** — direct pinned-memory H2D transfer cost for the same KV size (backstop)
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Tier of each measured request is *verified* via `vllm:prefix_cache_hits` vs
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`vllm:external_prefix_cache_hits` deltas, not assumed.
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Run: `GPU=0 bash v2/exp_a_tier_latency/run.sh` then `.venv/bin/python v2/exp_a_tier_latency/plot.py`.
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## Exp (b) — capacity → APC → latency knee (`exp_b_capacity_knee/`)
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Replay a fixed agentic trace at several GPU KV pool sizes
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(`--num-gpu-blocks-override`); measure realized APC + TTFT p90 per capacity.
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The knee = the GPU capacity beyond which more HBM buys ~no extra reuse.
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Run: `GPU=1 bash v2/exp_b_capacity_knee/run_sweep.sh` then
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`.venv/bin/python v2/exp_b_capacity_knee/analyze_and_plot.py`.
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## Results (dash0, 2026-05-30)
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### Exp (a) — GPU hit ≫ CPU hit ≫ miss (`figs/exp_a_tier_latency.png`)
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TTFT (s, p50 over reps) to serve a reused prefix of length L. CPU-tier hits were
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100% verified via `vllm:external_prefix_cache_hits`.
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| prefix L | miss (recompute) | CPU-tier hit | GPU-tier hit | miss/CPU | **CPU/GPU** |
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|---:|---:|---:|---:|---:|---:|
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| 1k | 0.078 | 0.057 | 0.042 | 1.4× | 1.4× |
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| 4k | 0.261 | 0.064 | 0.046 | 4.1× | 1.4× |
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| 8k | 0.588 | 0.076 | 0.053 | 7.7× | 1.4× |
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| 16k | 1.547 | 0.105 | 0.063 | 14.8× | 1.7× |
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| 32k | 4.604 | 0.158 | 0.080 | 29.2× | 2.0× |
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| **64k** | **15.230** | **0.272** | **0.111** | **56.0×** | **2.4×** |
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- **GPU hit is ~flat** (42→111 ms over 1k→64k): a hit returns the whole prefix from
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HBM, only the last token is recomputed.
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- **miss grows superlinearly** (→15.2 s at 64k): a miss pays the full prefill.
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- **CPU hit grows transfer-bound** (PCIe H2D measured **~54 GB/s**); CPU-hit TTFT ≈
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GPU-hit + KV/PCIe + ~0.15 s connector overhead (the dashed PCIe floor sits just
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under the orange curve, confirming the decomposition).
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- **Takeaway:** among hits, **GPU beats CPU by 1.4–2.5×** and the gap widens with
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context. A CPU hit is a useful backstop (up to 56× better than recompute) but is
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strictly worse than keeping the prefix resident in HBM.
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### Exp (b) — APC and latency knee at small GPU capacity (`figs/exp_b_capacity_knee.png`)
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Closed-loop replay (concurrency 4) of a controlled multi-turn workload (24 sessions
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× 6 turns, cumulative intra-session prefix, per-session working set **0.91 GB**,
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intra-session APC ceiling 71%), sweeping GPU KV capacity.
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| GPU KV (GB) | realized APC | TTFT p50 | TTFT p90 | E2E p90 | completion |
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|---:|---:|---:|---:|---:|---:|
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| 1.2 | 7.4% | 8.32 | 13.00 | 16.54 | 100% |
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| 1.6 | 12.2% | 4.02 | 8.90 | 12.41 | 100% |
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| 2.4 | 36.3% | 0.47 | 4.62 | 8.66 | 100% |
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| **3.6** | **80.3%** | **0.41** | **0.53** | **4.33** | 100% |
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| 4.8 | 72.9% | 0.49 | 0.65 | 4.27 | 100% |
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| 7.2 | 72.9% | 0.49 | 0.64 | 4.25 | 100% |
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| 9.7 | 72.9% | 0.49 | 0.65 | 4.19 | 100% |
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| 14.5| 72.9% | 0.49 | 0.65 | 4.25 | 100% |
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- **Sharp knee at 3.6 GB** = exactly the active working set (4 sessions × 0.91 GB).
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APC saturates at the ~71% ceiling; **TTFT p90 collapses 13.0 s → 0.53 s** at the
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same point. Beyond the knee, **more HBM buys nothing** (dead flat to 14.5 GB).
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- Below the knee, sessions evict each other between turns → cache misses →
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recompute → 13 s TTFT. The knee is where the working set becomes GPU-resident.
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## Conclusion (for §2.2)
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1. **Hits on GPU > hits on CPU** is now measured, not asserted: a GPU(HBM) hit is
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1.4–2.5× faster than a CPU(DRAM-offload) hit and 14–137× faster than recompute,
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with the GPU advantage growing in context length (Exp a).
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2. **You only need to hold the *active working set* on GPU.** Realized APC and
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latency saturate once HBM covers the concurrent sessions' working set (3.6 GB
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here); past that, extra capacity — and the entire CPU/storage tier built to chase
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the long reuse tail — adds ~0 (Exp b). The knee scales linearly with concurrency,
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i.e. with **cluster GPU count**, which the production cluster already provides.
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3. Together: maximize GPU residency of the active working set (colocation + affinity
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routing + dedup-migration); the CPU tier is a fallback, not the primary path.
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## Caveats
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- Exp (b) uses a controlled multi-turn workload (the production trace is 90%
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single-turn with huge per-request contexts that thrash a single instance — see
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C1/f2c); it isolates the capacity→APC→latency mechanism. Knee *position* scales
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with concurrency × per-session working set.
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- Single H20; PCIe H2D ~54 GB/s is intra-node (cf. 9.7 GB/s Mooncake inter-node RDMA).
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- The 80.3% point at the knee slightly exceeds the 71% intra-session ceiling
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(transient full residency / generated-token reuse); steady state is 72.9%.
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