PyTorch Distributed Training — ELI5

Imagine you need to read a 1,000-page book and write a summary by tomorrow. Alone, it’s impossible. But if you split the pages among 10 friends, each person reads 100 pages, and then you combine your notes — suddenly it’s doable by dinner.

Distributed training in PyTorch does this with GPUs. Instead of one GPU crunching through all the data, you spread the work across 2, 4, 8, or even thousands of GPUs.

The most common approach is called data parallelism. Every GPU gets a complete copy of the model. Each one processes a different chunk of training data at the same time. Then they compare notes — specifically, they share what they learned (the gradients) and all update their model copies identically. It’s like 8 students reading different chapters but all ending up with the same understanding.

For truly enormous models that don’t fit on a single GPU — like GPT-4 sized networks — there’s model parallelism. Here, the model itself is split across GPUs. One GPU handles the first few layers, the next handles the middle layers, and so on. Think of an assembly line where each worker handles one stage.

This is how companies like OpenAI and Google train their largest models. Without distributed training, GPT-3 would have taken roughly 350 years on a single GPU. With thousands of GPUs working together, it took weeks.

The one thing to remember: Distributed training makes AI possible at scale — it splits work across many GPUs so models that would take years to train on one machine can finish in days.