vLLM bi-directional KV transfer cuts TTFT in agentic workloads
Improve TTFT for multi-turn agentic workloads with bi-directional KV transfer in vLLM
KV cache transfer addresses the hidden cost of agentic loops: re-prefilling the same context on every turn, which inflates latency and GPU spend as conversations grow. Teams running self-hosted agents on vLLM can replicate the setup on EC2 P5en instances with EFA. The reported gains also give a concrete comparison point for choosing between Qwen3-32B and GLM-5-FP8 when latency per turn matters.
AWS published a guide on improving time to first token (TTFT) for multi-turn agentic workloads using bi-directional KV cache transfer in vLLM. The approach lets prefill and decode nodes reuse previously computed conversation context across turns instead of reprocessing it. On Amazon EC2 P5en instances with EFA networking, TTFT improved by up to 1.6x for Qwen3-32B and 3.3x for GLM-5-FP8. The setup targets latency reductions in agents whose context grows each turn.
Improve TTFT for multi-turn agentic workloads with bi-directional KV transfer in vLLM
Multi-turn agentic workloads reprocess growing conversation context each turn, driving up latency. See how bi-directional KV cache transfer lets prefill and decode nodes reuse previously computed context across turns, cutting time to first token by up to 1.6x for Qwen3-32B and 3.3x for GLM-5-FP8 on Amazon EC2 P5en instances with EFA.
