从啥也不会到DeepSpeed————一篇大模型分布式训练的学习过程总结 (English)

Generated: 2026-06-20 12:19:25

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Guess what? When I wrote my last GPT tutorial, my inbox was flooded with messages from readers: "Hey master, I've digested the model theory, but I want to train a 7B model. My single GPU just goes black. How does distributed training even work?"

Ah, to be honest, a year ago, I was just as lost. I thought distributed training was just plugging in a few more GPUs—simple! But when I actually tried it, I realized the rabbit hole goes deep.

Many tutorials either throw papers at you from the start, making your head spin, or they only talk about concepts without a single word on practical implementation.

So today, let's get down to business—I'm going to directly answer the four questions you care about most. These are hard-earned lessons, with all the pitfalls clearly marked. If I can save even one person, it's worth it.

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Question 1: Why do large models even need distributed training? Can't I just use a single GPU?

Two words: No way. For two reasons—either it won't fit, or it won't run.

Let's talk about memory first.

Take GPT-2, for example. 1.5B parameters,