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English(EN) Disaggregated Prefill-Decode in vLLM: NixlConnector Guide

NVIDIA NIXL 和 LMCache 通过 Disaggregated Prefill-Decode 降低 LLM 延迟

一种名为 Disaggregated Prefill-Decode 的新技术,利用 NVIDIA 的 NIXL 和 LMCache,显著提高了 LLM 推理性能,尤其是在长上下文任务中。该方法将计算密集型的 prefill 阶段与内存带宽受限的 decode 阶段分离到不同的 GPU 池中。通过使用 NIXL 和 GPUDirect RDMA 在高速网络上进行通信,它能够直接在 GPU 之间异步流式传输 KV 缓存数据,从而大大降低延迟并提高吞吐量。 AI

影响 将长上下文 LLM 任务的 token 间延迟降低 74%,并将 GPU 吞吐量提高 2.4 倍。

排序理由 对 LLM 推理基础设施的优化技术进行技术深度解析。[lever_c_demoted from research: ic=1 ai=1.0]

在 dev.to — LLM tag 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

NVIDIA NIXL 和 LMCache 通过 Disaggregated Prefill-Decode 降低 LLM 延迟

本文如何被排名

Signal score
40 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
对 LLM 推理基础设施的优化技术进行技术深度解析。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
infra
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
Breaking (< 6h)
Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

报道来源 [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Asif Muhammad Iqbal ·

    vLLM中的解耦预填充-解码:NixlConnector指南

    <blockquote> <p><em>Originally published by **Asif Muhammad Iqbal (Founder &amp; CEO at Logic42)</em>* at <a href="https://logic42.ai/articles/vllm-disaggregated-prefill-decode-nixlconnector-lmcache" rel="noopener noreferrer">https://logic42.ai/articles/vllm-disaggregated-prefill…