Push-based KV transfer to improve TTFT and TPOT for disaggregated inference in vLLM
vLLM's prefill-decode disaggregation splits LLM inference across engine instances but must transfer the KV cache between them. This post introduces a push-based NIXL connector that moves transfer coordination off the critical path, delivering up to 3x better TTFT and 30% better TPOT in benchmarks on Amazon EC2 P5en instances with AWS EFA.
What happened
vLLM's prefill-decode disaggregation splits LLM inference across engine instances but must transfer the KV cache between them. This post introduces a push-based NIXL connector that moves transfer coordination off the critical path, delivering up to 3x better TTFT and 30% better TPOT in benchmarks on Amazon EC2 P5en instances with AWS EFA.
Why it matters
The development may change operating conditions or market expectations around Amazon. Further confirmation and measurable outcomes matter.
Affected entities
View evidence
1 reports · 1 original report · 1 independent
- AWS Machine Learning BlogPrimary source · Supports · EN · 100%Push-based KV transfer to improve TTFT and TPOT for disaggregated inference in vLLM ↗
Claims
- Push-based KV transfer to improve TTFT and TPOT for disaggregated inference in vLLM Observed
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No material conflict detected in the available evidence.
Timeline
- First reported
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