# Improve TTFT for Multi-turn Agentic Workloads with Bi-directional KV Transfer in VLLM

> **Platform:** [SPIDITS AI](https://spidits.com/) — Real-Time AI News & Market Intelligence  
> **Published:** 2026-10-19T16:03:23.000Z  
> **Category:** PRODUCT_LAUNCH  
> **Impact Score:** 130/100  
> **Primary Source:** [AWS ML Blog](https://aws.amazon.com/blogs/machine-learning/improve-ttft-for-multi-turn-agentic-workloads-with-bi-directional-kv-transfer-in-vllm)  
> **Canonical Citation:** [https://spidits.com/timeline/improve-ttft-for-multi-turn-agentic-workloads-with-bi-directional-kv](https://spidits.com/timeline/improve-ttft-for-multi-turn-agentic-workloads-with-bi-directional-kv)

## Executive Summary
Multi-turn agentic workloads reprocess growing conversation context each turn, driving up latency.

## Why It Matters (Strategic Analysis)
Traditional disaggregated serving architectures suffer from high time-to-first-token latency in multi-turn conversations because decode nodes hold session states that prefill nodes cannot access, forcing full context recomputations upon cache eviction. Implementing bi-directional KV transfer over low-latency RDMA networks solves this bottleneck, enabling sustained accelerator utilization and predictable latency scaling for heavy agentic workloads.

## Referenced Coverage & Sources
- **[AWS ML Blog](https://aws.amazon.com/blogs/machine-learning/improve-ttft-for-multi-turn-agentic-workloads-with-bi-directional-kv-transfer-in-vllm)**: 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..._

---
*Synthesized by SPIDITS AI Market Intelligence Desk. Track live AI news, model releases, and funding: [https://spidits.com](https://spidits.com)*