Headline: KVC v2 + load-floor + RDMA beats naive PD-disagg on
mean/p50/p90 by 30-65% (TTFT p50 31s vs 88s, lat p50 37s vs 93s,
wall-clock 64 min vs 88 min). Loses p99 by ~8% (TTFT 224 vs 207).
Wrote 4 figures (docs/figures/):
e1_vs_e4_ttft_pdf.png — bimodal E4 fast-path peak vs E1 single peak
e1_vs_e4_latency_cdf.png — CDF + log-survival showing tail crossover
e4_path_latency.png — per-execution-mode latency breakdown
e1_vs_e4_p99_attribution.png — what makes up E4's p99 tail
P99 tail attribution (this is the key finding):
E4 p99 tail (n=65, TTFT ≥ 179.9s):
fast-path direct-to-d 0 % (0/65)
reseed paths 5 % (3/65)
fallback paths 88 % (57/65)
large-append-session-cap 43 % ← biggest culprit
no-d-capacity 17 %
large-append 14 %
Implication: D→P snapshot (designed to optimize reseed slow path)
even if fully working would touch ≤5% of the p99 tail. The real
bottleneck is *fallback chain* (admission retry + seeded-router
cold start), not reseed. Optimizing p99 needs work on fallback,
not more D→P plumbing.
Full analysis: docs/E4_VS_E1_RESULTS_ZH.md
Bar-overlap fix: extend ylim by 35-45% above the tallest bar to give the
"P GPU only sees 328 requests" and "P GPU does 1.07M tokens" annotations
clean white-bbox space above the bars instead of crashing into the KVC D
bars at x=1. Move both annotation xytext positions to x=2.4 (left panel)
and x=5.5 (right panel) so the arrows pull away from the orange P bar
toward the center of the panel.
Group labels (KVC 1P3D / DP 4-way CA) kept in axes-fraction bboxes at
y=1.02; subplot titles raised to pad=24 to leave room.
Note: a small visual collision between the bboxed group labels and the
subplot-title second line remains in the rendered output (acknowledged
in the prior conversation). Acceptable for now; full layout rework is
deferred. The annotation-vs-bar overlap (the original blocker) is fixed.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Two figures inserted into V2_DEEP_ANALYSIS §4.5 and §4.4 respectively, to
visually rebut the two critic-agent claims that we argued in prose were
design intent, not deficiencies.
(1) gpu_utilization.png -- §4.5 "P GPU is wasted 90% of the time"
Two-panel side-by-side:
Left (request count view, the naive reading): KVC P = 328 reqs (7.4%),
KVC D = ~1450 each, DP = ~1100 each. P "looks idle."
Right (compute work view, the honest reading): KVC P does 1.07M tokens
of prefill, comparable to each KVC D worker's ~0.80M. P is a
low-frequency high-cost safety net, not idle capacity.
Bonus finding: KVC's total compute (3.47M tokens across 4 GPUs) is 33%
LESS than DP's (5.17M). Same GPUs, less work done. That's the affinity
win.
(2) cache_efficiency.png -- §4.4 "Cache concentration is not policy win"
Two-panel side-by-side. The setup: KVC has 27% LESS total KV pool
(276K vs 351K tokens) yet caches MORE per request.
Left (cache hit rate vs turn number): KVC's session-affinity lets
hit rate accumulate with turns; DP's hash + radix-LRU causes
a mid-turn drift around turns 8-25 where KVC = 97.0% vs DP
= 95.8% (1.24pp gap). Shows mechanism, not just outcome.
Right (ECDF of per-request uncached tokens, log x): KVC's distribution
concentrates near zero (50% < 187 tokens), DP's is spread
(50% < 781 tokens). At uncached = 500 tokens threshold, KVC
has 74% of requests below, DP has 31%.
→ smaller pool, better retention, less per-request work. Direct empirical
rebuttal to "fragmentation is architectural, not policy."
Bundled scripts (rerunable):
- scripts/analysis/plot_gpu_utilization.py
- scripts/analysis/plot_cache_efficiency.py
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds a two-panel TTFT PDF comparison plot inserted as a new V2_DEEP_ANALYSIS
§3.4 ("TTFT 概率密度对比: bimodal vs unimodal"). Single-percentile numbers
(p50 / p99) hide the qualitative difference between the two distributions;
the figure makes it visible at a glance.
Left panel (linear x in [0, 0.6]s, body):
KVC has a sharp peak at ~40ms (the direct-to-D fast path).
DP has a broad peak around 50-200ms (full prefill per request).
Annotated with p50 and p90 markers for each side.
Right panel (log x in [10ms, 10s], full range):
KVC is visibly bimodal: a tall fast-path peak plus a small reseed tail
around 1-5s.
DP is unimodal: a single broad peak with shorter tail.
Annotated with p99 callouts pointing to each tail.
KDE: scipy.stats.gaussian_kde, bandwidth=0.15 for the body (Scott's rule
oversmooths the sharp fast-path peak), log10-transformed for the full-range
panel so the bimodal structure is visible.
Bundled:
- scripts/analysis/plot_ttft_pdf.py -- rerunable when v2 / DP data change.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
V2_DEEP_ANALYSIS §3.1 (execution_mode distribution) and §3.2 (path-level
latency vs DP) had hand-typed tables with approximate latencies (e.g.
"~1.0s") and required readers to mentally compare 5+ rows × 5 columns.
Both sections now reference generated PNG figures derived directly from
the v2 + DP metrics.jsonl files.
§3.1 figure (v2_execution_mode_distribution.png):
Horizontal bar chart, log x-axis. 4076 direct-to-D fast-path requests
(green) dwarf the rest by ~30x; the long tail of slow / fallback /
failure modes is visible at one glance. Counts and percentages
annotated on each bar.
§3.2 figure (v2_path_level_latency.png):
Grouped bar chart, log y-axis. Per-path TTFT p50 / TTFT p99 / Lat p50
with exact numeric labels (no more "~1.0s" approximations). Sample
counts annotated below each path. Quick visual reads:
- KVC fast path TTFT p50 41ms vs DP 92ms (2.2x faster)
- KVC reseed TTFT p99 5.12s vs DP 0.43s (12x slower) -- the cost
- KVC no-d-capacity TTFT p99 7.65s (worst case)
Bundled:
- scripts/analysis/plot_v2_path_breakdown.py -- the script that
generates both figures; rerunable when v2 data changes.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
