AWS Implements Bidirectional KV Cache Transfer in vLLM to Reduce Multi-Turn Agent TTFT
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On October 20, 2026, AWS published a post on the Machine Learning Blog introducing its bidirectional KV cache transfer technology implemented in vLLM. The technology targets P-D (prefill-decode) disaggregated deployments, allowing prefill nodes to reuse KV blocks retained by decode nodes, thereby eliminating the repeated prefill computation otherwise required in multi-turn conversations (multi-turn agent workloads). According to AWS, this optimization can reduce time-to-first-token (TTFT) for multi-turn agent workloads by up to 3.3x. This report is a first-hand source; no follow-up developments or conflicting information have been seen.
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- AWS Machine Learning BlogImprove TTFT for multi-turn agentic workloads with bi-directional KV transfer in vLLM
AWS introduces bi-directional KV cache transfer in vLLM, allowing prefill nodes in P-D disaggregated deployments to reuse KV blocks retained by decode nodes, reducing repeated prefill computation in multi-turn conversations.
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