AINewsnow

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…

Read the full story at AWS Machine Learning Blog ↗

Timeline · 1 report

  1. 2026-10-19 16:03 · AWS Machine Learning Blog
    Improve TTFT for multi-turn agentic workloads with bi-directional KV transfer in vLLM

More stories

  1. Google Open-Sources Harness2, a Recursive Agent Harnessing Framework — MarkTechPost
  2. GLM-5.3-Flash controlling robot fighters: the FP4 endpoint wins again, CoreWeave’s NVFP4 endpoint forfeits — r/LocalLLM
  3. Follow-up: Tested my 9 thinking rules on long-horizon agent tasks (Terminal-Bench 2.0) — r/PromptEngineering
  4. Shanghai AI Laboratory Releases Atria Dawn Full Version With a 512K Context Window — Pandaily
  5. DeepSeek V4.1 Flash 1017 now available on AI Gateway — Vercel Blog
  6. MiniMax launches M3.1 Flash, confirming Space Bunny was an early build — Vercel Blog
  7. Introducing TorchNeuron: Native PyTorch backend for AWS Trainium — PyTorch Blog
  8. Anthropic commits 5% of shares to public benefit fund ahead of planned IPO — TechCrunch AI

Get the daily brief of stories like this at 6:30 every morning →