Push-based KV transfer to improve TTFT and TPOT for disaggregated inference in vLLM
AWS Machine Learningen

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.
This is a short summary published by AI Global Wire. The full article is owned and hosted by AWS Machine Learning — open it there to read it in full.
Read the full story at AWS Machine LearningRelated AI news
- How Deepgram optimized voice AI at scale with AWS TrainiumAWS Machine Learning · October 19, 2026
- Improve TTFT for multi-turn agentic workloads with bi-directional KV transfer in vLLMAWS Machine Learning · October 19, 2026
- Building trust in AI agents: A production-ready evaluation framework with Amazon Bedrock AgentCoreAWS Machine Learning · October 16, 2026
- Introducing Reflection Beam: an open-weight model for agentic coding on AWSAWS Machine Learning · October 16, 2026
- Scaling Indic Pretraining: 2.8 Trillion Tokens and an Open Data Generation PipelineHugging Face · October 16, 2026
- Welcome Beam: a 501B open model you can own, customize, and scaleHugging Face · October 16, 2026