inference/research
verifiedhigh · 0.85updated 2026-10-03

Towards Load-Aware Prefill Deflection for Disaggregated LLM Serving (Kairos, arXiv:2607.02043)

paper

Fields

id
sources/paper-kairos-prefill-deflection-ttft-breakdown
Publisher
Microsoft Research
Kind
paper
Published
2026-07-02
Accessed
2026-10-03
notes
Kairos: proactive prefill-deflecting scheduler for disaggregated LLM serving on vLLM 0.18.1 with DeepSeek-V2-Lite on 2P2D (2 prefill + 2 decode nodes, each A100 80GB). Key finding: prefill execution accounts for only 2-23% of P95 TTFT; remaining 77-98% is prefill queue wait and inter-node KV-cache transfer. Five workloads tested (LPLD, LPHD, HPLD, HPHD, Bursty) with production-derived traces normalized to 32K context. Deflecting prefill to decode nodes (building KV cache in place) eliminates KV transfer and reduces P95 TTFT by up to 81%.

Notes

Kairos: proactive prefill-deflecting scheduler for disaggregated LLM serving on vLLM 0.18.1 with DeepSeek-V2-Lite on 2P2D (2 prefill + 2 decode nodes, each A100 80GB). Key finding: prefill execution accounts for only 2-23% of P95 TTFT; remaining 77-98% is prefill queue wait and inter-node KV-cache transfer. Five workloads tested (LPLD, LPHD, HPLD, HPHD, Bursty) with production-derived traces normalized to 32K context. Deflecting prefill to decode nodes (building KV cache in place) eliminates KV transfer and reduces P95 TTFT by up to 81%.

Cross-references

This record references no other records.