---
title: "Improve TTFT for multi-turn agentic workloads with bi-directional KV transfer in vLLM"
slug: "vllm-beschleunigt-agenten-workflows-mit-bidirektionalem-kv-cache-transfer"
date: 2026-10-19
category: tech-pub
tags: [agents, amazon]
language: en
sources_count: 1
featured: false
publisher: AInauten News
url: https://news.ainauten.com/en/story/vllm-beschleunigt-agenten-workflows-mit-bidirektionalem-kv-cache-transfer
---

# Improve TTFT for multi-turn agentic workloads with bi-directional KV transfer in vLLM

**Published**: 2026-10-19 | **Category**: tech-pub | **Sources**: 1

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## TL;DR

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

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## Summary

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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## Why it matters

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

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## Key Points

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

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## Nauti's Take

For teams running long agent sessions, this is primarily an infrastructure test: measure TTFT across ten or more turns and compare cache reuse, network overhead, and GPU utilization. AWS shows meaningful potential, yet the benchmark says little about your model mix, orchestration layer, or total cost. Start by checking whether transfer overhead actually reduces end-to-end waiting time in your workload.

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## FAQ

**Q:** What is Improve TTFT for multi-turn agentic workloads with bi-directional KV transfer in vLLM about?

**A:** Multi-turn agentic workloads reprocess growing conversation context each turn, driving up latency.

**Q:** Why does it matter?

**A:** Multi-turn agentic workloads reprocess growing conversation context each turn, driving up latency.

**Q:** What are the key takeaways?

**A:** Multi-turn agentic workloads reprocess growing conversation context each turn, driving up latency.

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## Related Topics

- [agents](https://news.ainauten.com/en/tag/agents)
- [amazon](https://news.ainauten.com/en/tag/amazon)

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## Sources

- [Improve TTFT for multi-turn agentic workloads with bi-directional KV transfer in vLLM](https://aws.amazon.com/blogs/machine-learning/improve-ttft-for-multi-turn-agentic-workloads-with-bi-directional-kv-transfer-in-vllm/) - AWS Machine Learning Blog

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## About This Article

This article is a synthesis of 1 sources, curated and summarized by AInauten News. We aggregate AI news from trusted sources and provide bilingual (German/English) coverage.

**Publisher**: [AInauten](https://www.ainauten.com) | **Site**: [news.ainauten.com](https://news.ainauten.com)

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*Last Updated: 2026-10-19*