vLLM Implements Bidirectional KV Cache Transfer to Slash TTFT Latency in Multi-Turn Agent Workloads

vLLM has introduced a bidirectional KV Cache transfer mechanism within its Prefill-Decode disaggregated architecture, specifically targeting the high Time-To-First-Token (TTFT) latency caused by redundant historical token computation in multi-turn Agent workloads. This breakthrough allows Decode nodes to share their complete session KV states back to Prefill nodes for reuse, significantly reducing inference overhead from context accumulation.

Cross-referenced reports confirm that traditional unidirectional transmission forced Prefill nodes to recalculate tokens generated in previous turns, wasting compute resources. The new mechanism aligns with the "long prefix repetition, short output" traffic pattern of Agents. Experts from Inferact highlight that prefix cache hit rates and KV Cache reuse efficiency are more critical than raw TPS in these scenarios, preventing the costly re-execution of prefill steps in every dialogue turn.

This optimization signifies a deepening adaptation of inference engines from general chatbots to autonomous agents. It directly alleviates infrastructure bottlenecks in complex tool-calling workflows, providing developers with core support for building low-latency, high-efficiency multi-turn interactive agents and reshaping AI compute allocation logic.