Fixed race condition in P instance selection (all going to inst_0).
P2P design: HEAVY requests prefill on least-loaded OTHER instance,
KV transfer via Mooncake, decode on session-sticky instance.
Result (200 req, fresh restart, vs baseline):
TTFT p50: 1.080 -> 0.939 (-13%) <- median improves (decode not disrupted)
TTFT p90: 9.410 -> 14.987 (+59%) <- tail worsens (KV transfer on large req)
TPOT p90: 0.076 -> 0.075 (-1%) <- unchanged (not the bottleneck)
E2E p50: 5.306 -> 5.565 (+5%) <- slightly worse overall
The P2P offload helps the common case (WARM/MEDIUM get lower TTFT because
their instance isn't blocked by a heavy prefill) but hurts HEAVY requests
(extra KV transfer latency). This is a median-vs-tail tradeoff.
For SLOs targeting p50: P2P offload helps.
For SLOs targeting p90/p99: baseline combined is better.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Implemented --offload mode: HEAVY requests (>20k new tokens) get P on
least-loaded instance, KV via Mooncake RDMA, D on session-sticky instance.
WARM/MEDIUM stay co-located (no KV transfer). All 8 instances run kv_both.
Result (200 req, same instances, fresh restart):
Baseline (no offload): TTFT=1.073 TPOT90=0.074 E2E=5.086
Offload HEAVY: TTFT=1.462 TPOT90=0.077 E2E=6.847
Delta: +36% +4% +35%
Conclusion: even selective KV transfer (only 44% of requests) adds more
overhead than the isolation benefit provides. On single-machine 8 GPU,
PD-combined with hybrid routing is strictly optimal. No form of KV
transfer — full PD-sep, selective offload, or otherwise — improves
over co-located serving for this workload.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Routing fix: new sessions placed by cumulative token load (greedy bin
packing) with cache-hit tiebreak. Session affinity for turn 2+.
Replayer now sends X-Session-Id header for proper session tracking.
Agentic workload core patterns (GLM-5.1 trace):
- 91% of reusable KV is intra-session (not cross-session)
- Session-sticky routing is THE critical optimization
- 36% warm requests (1.3k new tokens), 64% cold (17k+)
- After cache: effective prefill/decode ratio drops from 61.5x to 28.7x
- Cross-session sharing (system prompt) is only 4.8% of tokens
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Added --heavy-threshold to cache_aware_proxy.py. HEAVY requests (new
tokens >= threshold) route to instance with least decode load; WARM/MEDIUM
route by cache-hit + token-level LB as before.
Result: no significant difference vs baseline on single-machine combined mode.
TTFT: +1.2%, TPOT: -1.5%, E2E: -0.3% (all within noise)
Per-class TTFT breakdown shows the optimization target:
WARM (75 req): p50=0.198s (cache hit, nearly free)
MEDIUM (72 req): p50=1.356s
HEAVY (54 req): p50=7.124s (36x slower than WARM)
Conclusion: single-machine combined mode already distributes load well
enough that adaptive routing adds no benefit. True isolation of HEAVY
prefills requires cross-machine offload (v2 with Mooncake or multi-node).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Breakdown profiling at proxy level captures:
t_proxy_recv → t_prefill_sent → t_prefill_done → t_decode_sent → t_first_token
Key finding: 87.7% of TTFT is spent in kv+decode phase, NOT prefill.
Root cause: decode instance KV cache memory saturation (97.1% usage).
With 6P+2D config, 2 decode GPUs have only ~56GB total KV cache.
Large agentic requests (avg 33.6k tokens) fill this quickly.
Small requests (49 tokens, prefill=0.044s) wait 114s for KV cache
to be freed by large requests completing decode.
vLLM log confirms: Running=0, Waiting=6, KV cache=97.1%
GPU is idle but requests queue for KV cache memory, not compute.
This is the fundamental bottleneck of single-machine PD separation
for long-context agentic workloads: concentrating decode onto fewer
GPUs creates a KV cache memory wall.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Added --fire-and-forget flag to cache_aware_proxy.py for async prefill dispatch.
Results on 6P+2D config:
Await: TTFT=1.48s TPOT=0.066s E2E=5.95s 94% success
FnF: TTFT=5.32s TPOT=0.037s E2E=11.9s 85% success
Fire-and-forget improves TPOT by 44% (pipeline overlap) but degrades
TTFT by 260% (decode internally waits for KV, less efficiently than
proxy-level await) and increases errors from KV race conditions.
Full 4-way ablation summary in analyze_ablations.py.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Systematic study of prefill-decode disaggregation for agentic LLM workloads
using production GLM-5.1 coder trace (2.1M requests, 71B input tokens).
Key findings:
- Cache-aware routing improves TPOT p90 by 15% and APC from 20.8% to 44.7%
without PD separation, matching PD-Sep's decode isolation benefit
- PD separation adds +72% TTFT overhead (KV transfer) with no TPOT gain
when using the same cache-aware scheduler
- Prefill remains compute-bound even at 95% KV cache reuse (AI >1000x
vs decode AI <2), but absolute FLOPs drop 71% from cache hits
- For agentic MoE workloads, cache-aware routing > PD separation
Infrastructure:
- Trace sampler preserving session structure + hash_ids for prefix sharing
- Async trace replayer with streaming TTFT/TPOT/E2E measurement
- Unified cache-aware + token-level load-balanced global scheduler proxy
supporting both PD-colocated and PD-disaggregated (Mooncake/RDMA) modes
- vLLM 0.18.1 scheduler patch for KV transfer abort race condition
- Roofline analysis tool for prefill/decode compute characterization
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
