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>
