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        <title>Distributed Training - 标签 - vllbc02&#39;s blogs</title>
        <link>https://blog.vllbc.top/tags/distributed-training/</link>
        <description>Distributed Training - 标签 - vllbc02&#39;s blogs</description>
        <generator>Hugo -- gohugo.io</generator><language>zh-CN</language><copyright>This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.</copyright><lastBuildDate>Wed, 24 Jun 2026 00:00:00 &#43;0000</lastBuildDate><atom:link href="https://blog.vllbc.top/tags/distributed-training/" rel="self" type="application/rss+xml" /><item>
    <title>The Ultra-Scale Playbook 学习笔记</title>
    <link>https://blog.vllbc.top/ultrascale-playbook-%E7%AC%94%E8%AE%B0/</link>
    <pubDate>Wed, 24 Jun 2026 00:00:00 &#43;0000</pubDate>
    <author>vllbc</author>
    <guid>https://blog.vllbc.top/ultrascale-playbook-%E7%AC%94%E8%AE%B0/</guid>
    <description><![CDATA[<blockquote>
<p>来源：<a
href="https://huggingface.co/spaces/nanotron/ultrascale-playbook">The
Ultra-Scale Playbook: Training LLMs on GPU Clusters</a>（Hugging Face
nanotron 团队，2025）</p>
<p>BibTeX: Tazi et al., “The Ultra-Scale Playbook: Training LLMs on GPU
Clusters”, 2025.</p>
<p>配套仓库：<a
href="https://github.com/huggingface/nanotron">nanotron</a>（生产级）、<a
href="https://github.com/huggingface/picotron">picotron</a>（教学级）</p>
<p>原书基于 <strong>4100+ 组分布式实验、16k+ 测试运行</strong>
的实测数据写成，是当前最系统的分布式 LLM
训练手册。本文按主线整理：先把”为什么会爆显存”的代价讲清楚，再讲 5
种并行 + ZeRO
怎么对付这些代价，最后给出选型决策三步法。所有公式、表格、决策规则均保留原文细节。</p>]]></description>
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