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.
What happened
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.
Why it matters
The proceeding may create legal precedent, financial exposure or operating constraints for Amazon.
Affected entities
View evidence
1 reports · 1 original report · 1 independent
- AWS Machine Learning BlogPrimary source · Supports · EN · 100%Improve TTFT for multi-turn agentic workloads with bi-directional KV transfer in vLLM ↗
Claims
- Improve TTFT for multi-turn agentic workloads with bi-directional KV transfer in vLLM Observed
Conflicts
No material conflict detected in the available evidence.
Timeline
- First reported
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