A Practical Guide to Scaling Language Models: From Single Accelerators to Thousands
By
Jacob Austin
Summary
This article/book excerpt demystifies the science of scaling language models, explaining how TPUs and GPUs work, how they communicate, how LLMs run on real hardware, and how to parallelize models during training and inference for efficient operation at massive scale. It covers principles that apply from single accelerators to tens of thousands, aimed at readers with basic LLM and Transformer knowledge who want to optimize model performance.
Source
Twitter / XA Practical Guide to Scaling Language Models: From Single Accelerators to Thousandsjax-ml.github.ioKey quotes
· 4 pulledMuch of deep learning still boils down to a kind of black magic, but optimizing the performance of your models doesn't have to — even at huge scale!
Relatively simple principles apply everywhere — from dealing with a single accelerator to tens of thousands — and understanding them lets you do many useful things.
Training LLMs often feels like alchemy, but understanding and optimizing the performance of your models doesn't have to.
This book aims to demystify the science of scaling language models: how TPUs (and GPUs) work and how they communicate with each other, how LLMs run on real hardware, and how to parallelize your models during training and inference.
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