# Elastic Prefill Service: Hypotheses and Validation Log **Date**: 2026-05-23 **Context**: Investigating whether elastic PD disaggregation can improve agentic LLM serving vs pure co-located baseline. ## Baseline Reference (8C plain, fresh restart, 200 req) ``` OK=198/200 TTFT50=1.075 TTFT90=9.384 TPOT90=0.0761 E2E50=5.075 WARM: TTFT50=0.137 TPOT90=0.061 MEDIUM: TTFT50=0.921 TPOT90=0.079 HEAVY: TTFT50=4.945 TPOT90=0.076 ``` --- ## H1: Mooncake kv_both has significant runtime overhead **Claim**: Enabling kv_both mode degrades TPOT even without KV transfer (RDMA threads, ZMQ sockets compete for CPU). **Prior evidence**: Earlier elastic P2P experiment showed MEDIUM TPOT 0.079→0.197 (+150%). Attributed to kv_both overhead. **Experiment**: Phase 0A (7C kv_both, no offload) vs Phase 0B (7C plain) **Result**: TPOT90 = 0.0738 (kv_both) vs 0.0729 (plain) → **+1.3%, within noise** **Verdict**: **REJECTED**. kv_both has zero runtime overhead. The earlier 150% TPOT degradation was from offload-induced interference, not kv_both itself. --- ## H2: Dedicated Prefill Service (PS) without KV pull improves HEAVY TTFT **Claim**: A dedicated PS instance (no sessions) does HEAVY prefill without disrupting C's decode. PS does full cold prefill (no cache), D (session-sticky C) pulls KV and decodes. **Experiment**: PS V1 — 1PS + 7C kv_both, always offload HEAVY to PS **Result**: - `ps_always`: OK=195/200, HEAVY TTFT p50=~7.8s (baseline 5.0s, **+56%**), cascading timeouts - `ps_cost`: 0 offloads (cost model correctly identifies PS is more expensive) - `ps_flexd`: OK=172/186 (92.5%), HEAVY TTFT p50=7.8s, 12 ReadTimeout **Root cause**: PS has no KV cache for the session → full cold prefill is SLOWER than C's cached prefill. Cost model: `full_input/8333 > (input-cached)/8333 + interference` is always true. **Verdict**: **REJECTED**. PS without KV pull cannot beat cached co-located prefill. The cold prefill overhead + KV transfer time exceeds the interference savings. --- ## H3: C_s cached prefill + flexible D decode (V2) improves E2E **Claim**: C_s (session-sticky, has cache) does fast prefill (max_tokens=1), D (least-loaded C) pulls KV via Mooncake and does decode. Benefits: (1) C_s prefill is fast due to cache, (2) D is least-loaded so decode starts quickly, (3) session migrates to D for better load balance. **Experiment**: V2 — 8C kv_both, HEAVY offloaded (C_s prefill → flexible D decode) **Result**: ``` OK=179/185 (96.8%) TTFT50=0.762 (-29%) E2E50=4.628 (-9%) TPOT90=0.0746 (=) HEAVY: TTFT50=4.794 (≈baseline) TTFT90=20.4 (+117%) Routes: 63 HEAVY_OFFLOAD, 51 MEDIUM, 69 WARM Cache hit on offloaded: mean=3%, median=0% (92% are turn-1 cold) Prefill: p50=5.0s D KV pull: p50=1.1s p90=6.7s ``` **Partial validation**: E2E p50 improved 9%, TTFT p50 improved 29%. But HEAVY p90 degraded 2x and 6 errors (vs 2 baseline). **Key finding**: 92% of HEAVY requests are turn-1 (zero cache on C_s). C_s does COLD prefill anyway → offload adds pure RDMA overhead (~1.1s) with no cache benefit. **Verdict**: **PARTIALLY VALIDATED**. The architecture works for MEDIUM and WARM (better load balance). But blindly offloading all HEAVY hurts because most are cold. --- ## H4: Only offload HEAVY with high cache hit (cold HEAVY should stay co-located) **Claim**: Turn-1 HEAVY requests have zero cache → co-located is faster (no RDMA overhead). Only turn-2+ HEAVY with significant cache hit (>50%) should be offloaded, because: - C_s's prefill is fast (only new tokens computed) - D gets the KV via RDMA (~1.1s, small vs the savings from not waiting for C_s's decode queue) - C_s's decode is not disrupted **Counterintuition**: This challenges the conventional PD-sep assumption that "all heavy prefill should be disaggregated." For agentic workloads with high cache reuse (70%+), most of the "heavy" prefix is already cached — the actual compute is MEDIUM-level. **Experiment**: TODO — V2 with `cache_hit > 50% * input_length` gate **Expected**: - Turn-1 cold HEAVY stays co-located (no RDMA overhead, same TTFT as baseline) - Turn-2+ cached HEAVY gets offloaded (C_s fast prefill + D least-loaded decode) - Overall: HEAVY TTFT ≈ baseline, HEAVY TPOT improved (D less loaded), fewer errors --- ## H5: RDMA KV transfer overhead (1.1s p50) is too high — should be pipelined **Claim**: The 1.1s p50 KV transfer time for HEAVY requests (~40k tokens) seems excessive. At 200Gbps RDMA (25 GB/s), 40k tokens × 96KB/token = 3.75GB → should take ~0.15s. The 7x gap suggests block-by-block transfer without pipelining. **Questions to investigate**: 1. Does Mooncake do layerwise KV transfer? (transfer layer N while computing layer N+1) 2. Is the 1.1s from RDMA setup overhead, block scatter, or actual bandwidth? 3. Does vLLM's chunked prefill interact with the transfer (blocks only available after each chunk)? **From Mooncake code**: `MooncakeConnector does not do layerwise saving` (comment in code). All blocks are saved/loaded after the FULL prefill completes. This means: - Prefill must complete entirely before ANY KV transfer starts - D cannot start decode until ALL blocks arrive - No overlap between prefill compute and KV transfer **Potential optimization**: Layerwise transfer would allow D to start pulling layer 0's KV while C_s is still computing layer 47's KV. This could reduce the effective transfer latency to near zero (hidden behind compute). **Experiment**: TODO — Profile actual RDMA transfer time vs setup overhead. Check if `start_load_kv()` and `wait_for_layer_load()` APIs support layerwise loading (they exist in the interface but Mooncake doesn't implement them). --- ## H6: Session migration breaks KV cache locality for future turns **Claim**: When a HEAVY request is offloaded from C_s to D, session affinity moves to D. But D starts with zero cache for this session — it only has the KV from the current turn (transferred via RDMA). Future turns go to D, which now has the current turn cached. But the RDMA-transferred KV might not be properly registered in D's prefix cache. **Questions**: - Does vLLM's prefix cache recognize RDMA-transferred blocks as cacheable? - If yes, future turns on D should have similar APC to staying on C_s. - If no, every turn after migration is a cold start on D. **From vLLM metrics**: `external_prefix_cache_hits_total` counts cross-instance cache hits. If this is > 0 on D after migration, the transferred blocks ARE cacheable. **Experiment**: TODO — Track per-instance APC before and after session migration. Check if D's APC for migrated sessions matches expectations. --- ## Summary of Current Understanding ``` Turn 1 (cold) Turn 2+ (cached) ───────────── ──────────────── Co-located: ✅ Best (no overhead) ⚠️ HEAVY disrupts decode Offload (V2): ❌ Adds RDMA overhead ✅ C_s fast prefill + D load balance ``` The optimal strategy is **hybrid**: co-locate cold turn-1, offload cached turn-2+. This is the key insight for the paper: **the offload decision should be cache-aware, not size-based**. A 80k-token request with 90% cache hit is effectively a 8k-token prefill — MEDIUM, not HEAVY. The "heaviness" that matters for PD disaggregation is `new_tokens_to_compute`, not `total_input_length`. --- ## H7: OVERLOAD_FACTOR tuning improves GPU balance **Claim**: Lowering OVERLOAD_FACTOR (from 2.0 to 1.5/1.3/1.0) breaks session affinity earlier, improving GPU utilization balance. **Experiment**: 4 baseline runs (no Mooncake) with OF=2.0, 1.5, 1.3, 1.0. 200 req each, fresh restart. **Result**: ``` OF=2.0: imbalance=3.71x TTFT50=1.077 E2E50=5.093 OF=1.5: imbalance=3.45x TTFT50=1.068 E2E50=5.480 OF=1.3: imbalance=3.96x TTFT50=1.073 E2E50=5.144 OF=1.0: imbalance=3.47x TTFT50=1.085 E2E50=5.496 ``` All within noise. APC unchanged (~30%). **Verdict**: **REJECTED**. The imbalance is driven by workload skew (some sessions are inherently heavier), not by sticky routing. The OVERLOAD_FACTOR threshold rarely fires because per-instance load fluctuates too quickly. The hot GPU just rotates to different instances across runs. **Key learning**: The root cause of GPU imbalance is at **session placement time (turn 1)**, not at affinity-breaking time (turn 2+). Turn-1 placement uses `ongoing_tokens` scoring, which is a snapshot that doesn't account for cumulative or future load. --- ## H4 Validated: Cache-gate improves GPU balance but RDMA kills TTFT **Experiment**: H4 cache-gate (8C kv_both, offload only when cache_ratio >= 0.3) with GPU profiling. **Result**: ``` Baseline H4 cache-gate GPU Imbalance: 3.97x 2.04x ← 2x better balance GPU Std: 14.9% 6.7% ← less variance GPU Max: 63.3% 35.3% ← no extreme hotspot HEAVY_COLO TTFT: 7.02s 6.28s ← -10.5% from better balance! HEAVY_OFFLOAD TTFT: N/A 11.45s ← RDMA penalty OK/N: 198/200 198/200 ← same reliability ``` **Key finding**: The 10.5% HEAVY_COLO improvement proves GPU balance → better latency. But the 7 RDMA-offloaded requests (TTFT=11.45s) pull down the aggregate. RDMA transfer is bimodal: 3/7 fast (0.6-1.2s), 3/7 slow (18-31s). --- ## Current Understanding (updated) 1. **PD-Sep**: net negative (memory wall) ← proven 2. **LMetric**: ≈ baseline for agentic (session affinity limits routing freedom) ← proven 3. **Elastic P2P (RDMA)**: net negative on single machine (Mooncake lacks layerwise transfer → RDMA is pure overhead) ← proven 4. **OVERLOAD_FACTOR tuning**: no effect (imbalance from workload skew, not routing) ← proven 5. **GPU balance improvement → HEAVY TTFT -10.5%**: validated (H4 HEAVY_COLO data) 6. **The bottleneck is at time_scale=20 with 200 req**: system is only 30% loaded. Higher load may reveal more optimization opportunities. --- ## H8: Higher concurrency reveals prefill-decode interference **Claim**: At 8 sessions / 8 GPUs, the system is underloaded (30% GPU util). Increasing to 16 sessions should reveal prefill-decode interference. **Experiments**: - 8 sessions, ts=20, 1000 req: TPOT90=0.073, GPU=30%, imbal=1.5x - 16 sessions, ts=10, 500 req: TPOT90=0.106, GPU=~25%, imbal=~3.5x - 32 sessions, ts=10, 500 req: (not run yet) **Result**: ``` 8 sessions 16 sessions Delta TPOT p90: 0.0729 0.1058 +45%! WARM TPOT90: 0.0640 0.1301 +103%! MEDIUM TPOT90: 0.0750 0.1970 +149%! HEAVY TTFT50: (varies) 3.399 — E2E p50: 4.516 5.830 +29% ``` **Verdict**: **VALIDATED**. 16 sessions creates real prefill-decode interference. MEDIUM TPOT degrades 2.5x because HEAVY prefills (via chunked prefill) block decode steps on the same GPU. This is the scenario where PD disaggregation should theoretically help. --- ## H9: Elastic RDMA offload at 16 sessions reduces interference **Claim**: At 16 sessions where interference is severe, elastic V2 (C_s prefill + flexible D decode via RDMA) should reduce TPOT by isolating heavy prefill from decode. **Experiment**: 16 sessions, 500 req, elastic (kv_both + H4 cache-gate) **Result**: ``` Baseline 16s Elastic 16s Delta TPOT p90: 0.1058 0.1231 +16% (WORSE) MEDIUM TPOT90: 0.1970 0.2056 +4% (same) TTFT p50: 0.828 0.937 +13% (WORSE) E2E p50: 5.830 6.528 +12% (WORSE) OK/N: 498/500 498/500 same Offloaded: — 13/500 (2.6%) too few to matter ``` **Verdict**: **REJECTED**. Elastic at 16 sessions is WORSE, not better. Root causes: 1. Cache-gate correctly blocks 89% of HEAVY (cold turn-1, cache_ratio=0) → only 13 offloads 2. kv_both runtime overhead at high concurrency adds ~16% TPOT vs plain baseline 3. The 13 offloaded requests have TTFT p50=17.5s (RDMA overhead), much worse than colocated 3.5s **Key learning**: The RDMA transfer approach cannot solve prefill-decode interference because: - Most HEAVY are cold (no cache to benefit from offload) - Mooncake lacks layerwise transfer (RDMA is pure sequential overhead after prefill) - kv_both has non-zero overhead at high concurrency (contradicts Phase 0 at low concurrency) --- ## Current Understanding (final) ### What DOESN'T work for agentic workloads: 1. **PD-Sep**: net negative — KV cache memory wall on decode instances 2. **LMetric (OSDI'26)**: ≈ linear routing — `P_tokens` already includes `new_uncached_tokens`, so cache-hit scoring gives LMetric an implicit soft affinity that converges to similar placements as explicit sticky affinity (see `analysis/research_findings.md` §2.2 for the corrected framing) 3. **Elastic P2P RDMA offload**: net negative — Mooncake transfer overhead, no layerwise pipeline 4. **OVERLOAD_FACTOR tuning**: no effect — imbalance from workload skew, not routing 5. **Dedicated Prefill Service (PS)**: cannot win cost comparison without KV pull, PS is always slower than cached C 6. **Cache-gate offload (H4)**: correct but only 10-12% of HEAVY have cache → limited activation ### What DOES work: 1. **Cache-aware session-sticky routing**: +24pp APC, -60% TTFT vs round-robin (the dominant optimization) 2. **GPU balance from offload routing**: HEAVY_COLO -10.5% TTFT when imbalance reduced (H4 data) ### The real bottleneck: At production-level concurrency (>1 session/GPU), the dominant bottleneck is **chunked prefill interference**: large HEAVY prefill chunks block decode steps on the same GPU, causing TPOT to degrade 45-149%. Neither routing nor RDMA-based PD disaggregation solves this. The root cause is vLLM's scheduler design: - Chunked prefill chunk size (`max_num_batched_tokens`, default 8192) is fixed - Large prefill chunks monopolize the GPU for tens of ms, stalling decode - Reducing chunk size would improve decode responsiveness but increase prefill overhead ### Next direction: Adaptive chunked prefill scheduling Instead of fixed chunk size, dynamically adjust based on decode pressure: - When decode queue is deep: smaller chunks → more decode slots → better TPOT - When decode queue is empty: larger chunks → faster prefill → better TTFT - This is a vLLM scheduler modification, not a routing change --- ## Current routing direction (cross-reference) The hypotheses above produced the following positive results that informed the current `--policy unified` implementation: - H1 / H7 / H9 (negative): PD-sep offload, OVERLOAD_FACTOR tuning, and elastic RDMA at high concurrency all regressed or stayed within noise. - H3 / H4 / H6 (partial): cache-gated offload exists but only ~10-12% of HEAVY requests have cache, and the offloaded subset pays RDMA penalty. The active algorithm (commit `255c8e6`) is **hybrid LMetric + high-cache affinity** in baseline mode (no Mooncake). The retired migration variants are catalogued in `docs/migration-policy-design.md` (Approach A and the revert chain `cc6e562` / `4c583f2`). H7's rejection (OVERLOAD_FACTOR within noise) is why the active default stays at `overload_factor=2.0`.