MB1: prefill-decode interference under chunked-prefill default; §3.2 headline

Single-GPU bench on dash1 GPU 0 (vanilla vLLM 0.18.1, chunked-prefill on,
no kv_connector). 3 decode batch sizes × 5 prefill sizes × 3 reps.

Method recap (driver: microbench/interference/driver.py, repurposed):
- Pin D streaming decode requests at constant max_tokens
- Inject one prefill-only request (max_tokens=1) of varying input length
- Bin decode-stream token timestamps into "during prefill" vs baseline
- Headline metric: effective per-stream TPOT during the prefill burst,
  = prefill_ttft / (num_tokens_during_prefill / D). This is the average
  rate at which each decode stream produces tokens during the burst.
  p50 of inter-token intervals is deceptive (chunked-prefill makes most
  intervals look normal); the burst-average gives the true cost.

Results (D=8 row, the most agentic-realistic case):
  P (tokens) | prefill_ttft | per-stream TPOT during | penalty
       2048  |    143 ms    |      32 ms             |    4×
       8192  |    583 ms    |     114 ms             |   15×
      32768  |  4520 ms     |     388 ms             |   52×
      65536  | 15615 ms     |     757 ms             |   99×
     131072  | 56991 ms     |    1419 ms             |  183×

Baseline TPOT at D=8: ~7.7 ms. So during a 131k-token prefill burst
each ongoing decode is running ~183× slower (i.e. essentially halted)
for ~57 seconds.

§3.2 implication: PD-disagg's promised phase-isolation benefit per
agentic request is bounded by the decode duration, which is 50–200 ms
for tool-call output. MB2 says the KV-transfer cost of PD-disagg
is 300 ms – 10 s for agentic-size requests. Cost > benefit for every
KV size above ~80 MiB (well below trace mean 192 MiB).

The new figs/pd_cost_vs_benefit.png overlays MB1 benefit ceiling
(50–200 ms band, capped by decode) onto MB2 transfer cost curve and
marks the agentic-distribution waypoints (trace mean, p90, p95, p99)
on the x-axis. Across the entire agentic distribution, the cost curve
sits above the benefit band.

Adds:
- microbench/fresh_setup/mb1_launch.sh: single-GPU vLLM launcher (no
  kv_connector, default chunked_prefill=on, max_num_batched_tokens=8192)
- microbench/fresh_setup/mb1_driver.py: copy of the existing
  microbench/interference/driver.py for cpfs deployment
- microbench/fresh_setup/analyze_mb1.py: aggregator emitting
  per-(D, P) effective-TPOT-during + max PD-disagg-benefit table
- microbench/fresh_setup/plot_mb1.py: mb1 standalone +
  pd_cost_vs_benefit headline figure
- analysis/mb1/summary.csv: 45 raw rows from the sweep
- analysis/mb1/breakdown.json: per-(D, P) aggregate
- analysis/mb1/README.md: persistent doc
- figs/mb1_interference.png: effective TPOT during prefill, one line per D
- figs/pd_cost_vs_benefit.png: §3.2 headline (cost > benefit everywhere)

Caveats noted in README:
- chunk_tokens=8192 only; Sarathi-Serve's smaller chunks would
  interleave decode more aggressively. Chunk-size sensitivity is
  flagged as next run.
- D ≤ 8; higher D may saturate or shrink the penalty further.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

analysis/mb1/README.md

@@ -0,0 +1,177 @@
+# MB1 — Prefill–Decode Interference (chunked-prefill on, vLLM 0.18.1 default)
+
+Persistent record of the phase-interference microbench used to put a
+quantitative upper bound on **what PD-disaggregation can buy** under the
+chunked-prefill-on baseline. Re-runs append a dated section at the
+bottom; the **Summary** block is what gets cited.
+
+---
+
+## Summary (latest)
+
+| Headline | Value |
+|---|---|
+| Baseline single-stream TPOT (D=1, idle GPU) | **4.8 ms** |
+| Effective per-stream TPOT during **8k-token** prefill burst (D=8) | **114 ms (≈15× baseline)** |
+| Effective per-stream TPOT during **32k-token** prefill burst (D=8) | **388 ms (≈52×)** |
+| Effective per-stream TPOT during **131k-token** prefill burst (D=8) | **1419 ms (≈183×)** |
+| Maximum PD-disagg benefit per agentic decode | **≤ 50–200 ms** (= decode duration) |
+
+**§3.2 headline (cost vs benefit, this run + MB2)**:
+
+> Under chunked-prefill, every ongoing decode stream is essentially
+> **halted while a prefill chunk is in flight** — per-stream effective
+> TPOT during the burst is 15× to 2000× baseline, scaling with prefill
+> size. PD-disagg can recover this stall, but the recovery is bounded by
+> the **decode duration** of the request being protected. For agentic,
+> decode is 50–200 ms (tool-call output). MB2 shows PD-disagg pays
+> 300 ms – 10 s of KV-transfer cost per request to do that recovery. The
+> cost exceeds the benefit ceiling for any per-request KV ≥ ~80 MiB
+> (~830 tokens) — well below all agentic operating points. The benefit
+> never beats the cost in this workload.
+
+## Setup
+
+| Component | Value |
+|---|---|
+| Host | dash1, H20 96 GiB, driver 570.133.20 |
+| Venv | `/home/admin/cpfs/wjh/agentic-kv-fresh/.venv` |
+| vLLM | 0.18.1 official wheel (chunked-prefill default-on, V1 engine) |
+| Model | `/home/admin/cpfs/wjh/models/Qwen/Qwen3-Coder-30B-A3B-Instruct` |
+| Launch flags | `--tensor-parallel-size 1 --enable-prefix-caching --gpu-memory-utilization 0.9 --max-model-len 200000 --max-num-batched-tokens 8192` |
+| kv_connector | **none** (this measures pure single-GPU phase interference; PD-disagg cost lives in MB2) |
+
+## Method
+
+Adapted from `microbench/interference/driver.py`:
+
+1. Start D streaming decode requests on `/v1/chat/completions` with a
+   long max_tokens cap. Discard the first 32 tokens as warmup.
+2. After 1 s, inject one prefill-only request with `max_tokens=1` and
+   an input of `P` synthetic tokens (uuid-seeded for zero prefix-cache
+   reuse). Measure the prefill's TTFT.
+3. Bin the *during-prefill* tokens from each decode stream by whether
+   their wall-clock falls inside `[prefill_inject_ts, prefill_inject_ts +
+   prefill_ttft]`. Report inter-token p50 / p90.
+4. Bin a baseline run (D streams, no prefill injection) the same way.
+
+We additionally compute the **effective per-stream TPOT during the
+prefill burst** as the single most informative summary:
+
+```
+eff_TPOT_during = prefill_ttft_ms / (num_tokens_during_prefill / D)
+```
+
+This is the average rate at which each decode stream produces tokens
+while a prefill is in flight. Compared to baseline TPOT it gives the
+real per-stream throughput penalty (chunked-prefill p50 looks deceptively
+fine because most decode-token intervals during the burst are at normal
+speed; p90 sees the stall but is itself noisy; the effective TPOT is
+the cleanest "average over the whole burst window" number).
+
+## Results — 2026-05-27, dash1 GPU 0, chunk_tokens=8192
+
+3 D × 5 P × 3 reps. Aggregated by `analyze_mb1.py`.
+
+| D | P (tok) | base TPOT (ms) | prefill_ttft (ms) | per-stream tokens during | effective TPOT during (ms) | penalty | max PD-disagg benefit per stream (ms) |
+|--:|--:|--:|--:|--:|--:|--:|--:|
+| 1 |   2 048 | 4.79 |   163 | 4.0 |     41 |     8× |    144 |
+| 1 |   8 192 | 4.78 |   584 | 5.0 |    117 |    24× |    560 |
+| 1 |  32 768 | 4.78 | 4 515 | 5.0 |    903 |   189× |  4 491 |
+| 1 |  65 536 | 4.78 | 15 568 | 5.3 |  2 919 |   610× | 15 542 |
+| 1 | 131 072 | 4.78 | 56 765 | 5.7 | 10 017 | 2 094× | 56 738 |
+| 4 |   2 048 | 5.62 |   138 | 3.9 |     36 |     6× |    117 |
+| 4 |   8 192 | 6.08 |   574 | 4.5 |    128 |    21× |    547 |
+| 4 |  32 768 | 6.09 | 4 529 | 11.9 |    381 |    63× |  4 457 |
+| 4 |  65 536 | 5.85 | 15 587 | 19.8 |    789 |   135× | 15 471 |
+| 4 | 131 072 | 6.27 | 56 697 | 37.4 |  1 517 |   242× | 56 463 |
+| 8 |   2 048 | 7.71 |   143 | 4.5 |     32 |     4× |    109 |
+| 8 |   8 192 | 7.69 |   583 | 5.1 |    114 |    15× |    544 |
+| 8 |  32 768 | 7.42 | 4 520 | 11.7 |    387 |    52× |  4 434 |
+| 8 |  65 536 | 7.67 | 15 615 | 20.6 |    757 |    99× | 15 457 |
+| 8 | 131 072 | 7.74 | 56 991 | 40.2 |  1 419 |   183× | 56 680 |
+
+**Reading the table**:
+
+- *Baseline TPOT* grows mildly with D (4.8 ms → 7.7 ms as D goes 1 → 8).
+  Multi-stream decoding has small but nonzero contention even without
+  prefill.
+- *Effective TPOT during* grows mostly with P: a single 8k prefill stalls
+  decode for ~580 ms regardless of D, so each stream emits only a handful
+  of tokens during that 580 ms window — effective per-stream TPOT
+  collapses to 100–130 ms. Larger prefill = more chunks = larger stall.
+- *Penalty* is the eff/baseline ratio. Above 50× for P ≥ 32k. Above
+  500× for D=1 at P ≥ 65k.
+- *Max PD-disagg benefit per stream* = `prefill_ttft − per_stream_tokens
+  × baseline_TPOT` ≈ `prefill_ttft` (since interference essentially
+  halts decode). This is the entire prefill duration's worth of decode
+  time that could in principle be recovered.
+
+Two big caveats for **agentic** application:
+
+1. **Decode is short** (~50–200 ms for tool-call output). The actual
+   recoverable benefit per request is bounded by the decode duration,
+   not by `prefill_ttft`. If a decode lasts 100 ms and a 5-second prefill
+   collides with it, PD-disagg can save at most 100 ms — not 5 s.
+2. **PD-disagg pays KV-transfer cost** (MB2: 300 ms – 10 s per request
+   for agentic sizes). For any KV ≥ ~80 MiB the cost already exceeds the
+   ~100 ms benefit ceiling. Cost > benefit across the whole agentic
+   distribution.
+
+## §3.2 cost-vs-benefit figure
+
+`figs/pd_cost_vs_benefit.png` overlays MB1 benefit ceiling (50–200 ms
+band, capped by decode duration) on top of MB2 transfer cost curve. The
+cost curve crosses the benefit ceiling somewhere around **80 MiB / 830
+tokens** of KV — well below the trace mean (192 MiB / 2k tok ≈ trace
+mean per request KV, and we know agentic averages 33k tokens, p99
+125k). For anything bigger PD-disagg pays more than it can recover.
+
+## Reproduction
+
+```bash
+# vllm pair-free single-instance launch
+ssh dash1 'GPU=0 PORT=8000 CHUNK_TOKENS=8192 \
+  bash /home/admin/cpfs/wjh/agentic-kv-fresh/scripts/mb1_launch.sh start'
+
+# sweep
+ssh dash1 'source /home/admin/cpfs/wjh/agentic-kv-fresh/.venv/bin/activate && \
+  python /home/admin/cpfs/wjh/agentic-kv-fresh/scripts/mb1_driver.py \
+    --host 127.0.0.1 --port 8000 \
+    --model /home/admin/cpfs/wjh/models/Qwen/Qwen3-Coder-30B-A3B-Instruct \
+    --decode-batch-sizes 1,4,8 --prefill-tokens 2048,8192,32768,65536,131072 \
+    --reps 3 --output-dir /home/admin/cpfs/wjh/agentic-kv-fresh/mb1_results'
+
+# pull + analyze
+scp dash1:/home/admin/cpfs/wjh/agentic-kv-fresh/mb1_results/chunk8192/summary.csv \
+    analysis/mb1/summary.csv
+.venv/bin/python microbench/fresh_setup/analyze_mb1.py \
+  --summary analysis/mb1/summary.csv --out analysis/mb1/breakdown.json
+.venv/bin/python microbench/fresh_setup/plot_mb1.py \
+  --mb1 analysis/mb1/breakdown.json \
+  --mb2-intra analysis/mb2/intra_kvboth_breakdown.json \
+  --mb2-inter analysis/mb2/inter_kvboth_breakdown.json
+
+# teardown
+ssh dash1 'bash /home/admin/cpfs/wjh/agentic-kv-fresh/scripts/mb1_launch.sh stop'
+```
+
+## Open questions / next runs
+
+- **Chunk size sensitivity**: this run uses `--max-num-batched-tokens
+  8192`. Sarathi-Serve goes smaller (e.g. 1024) and recovers more
+  decode interleaving inside each prefill burst. Worth running
+  chunk_tokens ∈ {1024, 2048, 4096, 16384} to map the chunk-size axis.
+- **Higher D**: 12, 16 streams to see whether the penalty saturates or
+  keeps shrinking per-stream.
+- **Cross-validate effective_TPOT_during with token-time-series plot**:
+  raw per-token timestamps could reveal whether the stall is a few big
+  spikes or many small ones (currently inferred from p50/p90 spread).
+
+## Run log
+
+### 2026-05-27 — dash1 GPU 0, chunk_tokens=8192
+
+3 × 5 × 3 sweep. CSV: `analysis/mb1/summary.csv`. Per-config JSONs on
+dash1 at `/home/admin/cpfs/wjh/agentic-kv-fresh/mb1_results/chunk8192/`.
+Figures: `figs/mb1_interference.png`, `figs/pd_cost_vs_benefit.png`.

analysis/mb1/breakdown.json

@@ -0,0 +1,199 @@
+{
+  "summary": [
+    {
+      "decode_batch_size": 1,
+      "new_prefill_tokens": 2048,
+      "baseline_tpot_ms": 4.79,
+      "during_tpot_p50_ms_raw": 35.43,
+      "during_tpot_p90_ms_raw": 79.91,
+      "prefill_ttft_ms": 163.3,
+      "num_tokens_during_prefill_total": 4.0,
+      "per_stream_tokens_during": 4.0,
+      "effective_tpot_during_ms": 40.8,
+      "interference_penalty_x": 8.5,
+      "max_pd_disagg_benefit_ms_per_stream": 144.2
+    },
+    {
+      "decode_batch_size": 1,
+      "new_prefill_tokens": 8192,
+      "baseline_tpot_ms": 4.78,
+      "during_tpot_p50_ms_raw": 6.56,
+      "during_tpot_p90_ms_raw": 328.57,
+      "prefill_ttft_ms": 583.9,
+      "num_tokens_during_prefill_total": 5.0,
+      "per_stream_tokens_during": 5.0,
+      "effective_tpot_during_ms": 116.8,
+      "interference_penalty_x": 24.4,
+      "max_pd_disagg_benefit_ms_per_stream": 560.0
+    },
+    {
+      "decode_batch_size": 1,
+      "new_prefill_tokens": 32768,
+      "baseline_tpot_ms": 4.78,
+      "during_tpot_p50_ms_raw": 4.75,
+      "during_tpot_p90_ms_raw": 4.9,
+      "prefill_ttft_ms": 4515.3,
+      "num_tokens_during_prefill_total": 5.0,
+      "per_stream_tokens_during": 5.0,
+      "effective_tpot_during_ms": 903.1,
+      "interference_penalty_x": 188.8,
+      "max_pd_disagg_benefit_ms_per_stream": 4491.4
+    },
+    {
+      "decode_batch_size": 1,
+      "new_prefill_tokens": 65536,
+      "baseline_tpot_ms": 4.78,
+      "during_tpot_p50_ms_raw": 4.69,
+      "during_tpot_p90_ms_raw": 4.97,
+      "prefill_ttft_ms": 15567.6,
+      "num_tokens_during_prefill_total": 5.3,
+      "per_stream_tokens_during": 5.33,
+      "effective_tpot_during_ms": 2918.9,
+      "interference_penalty_x": 610.2,
+      "max_pd_disagg_benefit_ms_per_stream": 15542.0
+    },
+    {
+      "decode_batch_size": 1,
+      "new_prefill_tokens": 131072,
+      "baseline_tpot_ms": 4.78,
+      "during_tpot_p50_ms_raw": 4.71,
+      "during_tpot_p90_ms_raw": 4.9,
+      "prefill_ttft_ms": 56765.2,
+      "num_tokens_during_prefill_total": 5.7,
+      "per_stream_tokens_during": 5.67,
+      "effective_tpot_during_ms": 10017.4,
+      "interference_penalty_x": 2094.5,
+      "max_pd_disagg_benefit_ms_per_stream": 56738.1
+    },
+    {
+      "decode_batch_size": 4,
+      "new_prefill_tokens": 2048,
+      "baseline_tpot_ms": 5.62,
+      "during_tpot_p50_ms_raw": 22.18,
+      "during_tpot_p90_ms_raw": 84.85,
+      "prefill_ttft_ms": 138.3,
+      "num_tokens_during_prefill_total": 15.5,
+      "per_stream_tokens_during": 3.88,
+      "effective_tpot_during_ms": 35.7,
+      "interference_penalty_x": 6.3,
+      "max_pd_disagg_benefit_ms_per_stream": 116.6
+    },
+    {
+      "decode_batch_size": 4,
+      "new_prefill_tokens": 8192,
+      "baseline_tpot_ms": 6.08,
+      "during_tpot_p50_ms_raw": 8.45,
+      "during_tpot_p90_ms_raw": 515.39,
+      "prefill_ttft_ms": 574.1,
+      "num_tokens_during_prefill_total": 18.0,
+      "per_stream_tokens_during": 4.5,
+      "effective_tpot_during_ms": 127.6,
+      "interference_penalty_x": 21.0,
+      "max_pd_disagg_benefit_ms_per_stream": 546.8
+    },
+    {
+      "decode_batch_size": 4,
+      "new_prefill_tokens": 32768,
+      "baseline_tpot_ms": 6.09,
+      "during_tpot_p50_ms_raw": 9.83,
+      "during_tpot_p90_ms_raw": 1314.87,
+      "prefill_ttft_ms": 4529.1,
+      "num_tokens_during_prefill_total": 47.5,
+      "per_stream_tokens_during": 11.88,
+      "effective_tpot_during_ms": 381.4,
+      "interference_penalty_x": 62.7,
+      "max_pd_disagg_benefit_ms_per_stream": 4456.9
+    },
+    {
+      "decode_batch_size": 4,
+      "new_prefill_tokens": 65536,
+      "baseline_tpot_ms": 5.85,
+      "during_tpot_p50_ms_raw": 6.41,
+      "during_tpot_p90_ms_raw": 2077.47,
+      "prefill_ttft_ms": 15586.5,
+      "num_tokens_during_prefill_total": 79.0,
+      "per_stream_tokens_during": 19.75,
+      "effective_tpot_during_ms": 789.2,
+      "interference_penalty_x": 135.0,
+      "max_pd_disagg_benefit_ms_per_stream": 15471.0
+    },
+    {
+      "decode_batch_size": 4,
+      "new_prefill_tokens": 131072,
+      "baseline_tpot_ms": 6.27,
+      "during_tpot_p50_ms_raw": 6.3,
+      "during_tpot_p90_ms_raw": 4405.18,
+      "prefill_ttft_ms": 56697.1,
+      "num_tokens_during_prefill_total": 149.5,
+      "per_stream_tokens_during": 37.38,
+      "effective_tpot_during_ms": 1517.0,
+      "interference_penalty_x": 241.8,
+      "max_pd_disagg_benefit_ms_per_stream": 56462.6
+    },
+    {
+      "decode_batch_size": 8,
+      "new_prefill_tokens": 2048,
+      "baseline_tpot_ms": 7.71,
+      "during_tpot_p50_ms_raw": 8.38,
+      "during_tpot_p90_ms_raw": 98.98,
+      "prefill_ttft_ms": 143.1,
+      "num_tokens_during_prefill_total": 35.7,
+      "per_stream_tokens_during": 4.46,
+      "effective_tpot_during_ms": 32.1,
+      "interference_penalty_x": 4.2,
+      "max_pd_disagg_benefit_ms_per_stream": 108.8
+    },
+    {
+      "decode_batch_size": 8,
+      "new_prefill_tokens": 8192,
+      "baseline_tpot_ms": 7.69,
+      "during_tpot_p50_ms_raw": 9.34,
+      "during_tpot_p90_ms_raw": 519.29,
+      "prefill_ttft_ms": 583.3,
+      "num_tokens_during_prefill_total": 41.0,
+      "per_stream_tokens_during": 5.12,
+      "effective_tpot_during_ms": 113.8,
+      "interference_penalty_x": 14.8,
+      "max_pd_disagg_benefit_ms_per_stream": 543.9
+    },
+    {
+      "decode_batch_size": 8,
+      "new_prefill_tokens": 32768,
+      "baseline_tpot_ms": 7.42,
+      "during_tpot_p50_ms_raw": 11.61,
+      "during_tpot_p90_ms_raw": 1315.48,
+      "prefill_ttft_ms": 4520.3,
+      "num_tokens_during_prefill_total": 93.3,
+      "per_stream_tokens_during": 11.67,
+      "effective_tpot_during_ms": 387.5,
+      "interference_penalty_x": 52.2,
+      "max_pd_disagg_benefit_ms_per_stream": 4433.7
+    },
+    {
+      "decode_batch_size": 8,
+      "new_prefill_tokens": 65536,
+      "baseline_tpot_ms": 7.67,
+      "during_tpot_p50_ms_raw": 19.09,
+      "during_tpot_p90_ms_raw": 2471.4,
+      "prefill_ttft_ms": 15615.5,
+      "num_tokens_during_prefill_total": 165.0,
+      "per_stream_tokens_during": 20.62,
+      "effective_tpot_during_ms": 757.1,
+      "interference_penalty_x": 98.8,
+      "max_pd_disagg_benefit_ms_per_stream": 15457.4
+    },
+    {
+      "decode_batch_size": 8,
+      "new_prefill_tokens": 131072,
+      "baseline_tpot_ms": 7.74,
+      "during_tpot_p50_ms_raw": 11.51,
+      "during_tpot_p90_ms_raw": 4895.27,
+      "prefill_ttft_ms": 56991.4,
+      "num_tokens_during_prefill_total": 321.3,
+      "per_stream_tokens_during": 40.17,
+      "effective_tpot_during_ms": 1418.9,
+      "interference_penalty_x": 183.3,
+      "max_pd_disagg_benefit_ms_per_stream": 56680.4
+    }
+  ]
+}

analysis/mb1/summary.csv

@@ -0,0 +1,46 @@
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