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.
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Qué ocurrió
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.
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- AWS Machine Learning BlogFuente primaria · Respalda · EN · 100%Improve TTFT for multi-turn agentic workloads with bi-directional KV transfer in vLLM ↗
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