Improve TTFT for multi-turn agentic workloads with bi-directional KV transfer in vLLM

Key points

• In Prefill-decode (P-D) disaggregation, each request is split across specialized nodes: the prefill node processes the prompt and builds its key-value (KV) cache, then the decode node retrieves those KV blocks and generates the response.1
• When you build conversational assistants and agentic applications, longer interaction histories can increase latency and consume accelerator capacity through repeated prefill computation.1
• In this post, we show how bi-directional KV cache transfer lets prefill and decode nodes reuse previously computed context across turns, which improves accelerator utilization and helps applications sustain responsive TTFT as conversations grow.1
• Bi-directional KV transfer is a mechanism in which a decode node returns previously computed KV to a prefill node on subsequent turns of a conversation.1

Sources (1)

• [1]Improve TTFT for multi-turn agentic workloads with bi-directional KV transfer in vLLM
  AWS Machine Learning Blog · Oct 19, 04:03 PM

  Multi-turn agentic workloads build on accumulated context as an agent reasons, invokes tools, and incorporates earlier results.

  In Prefill-decode (P-D) disaggregation, each request is split across specialized nodes: the prefill node processes the prompt and builds its key-value (KV) cache, then the decode node retrieves those KV blocks and generates the response.

Extractive summary: sentences quoted from the sources.