jax-ml/scaling-book

What it solves

This book demystifies the complex process of scaling Large Language Models (LLMs) specifically on TPU hardware. It addresses the challenge of understanding how LLMs operate at scale and how to select effective parallelism strategies to prevent communication bottlenecks during training and inference.

How it works

It provides a comprehensive educational resource that explains the underlying mechanics of TPUs and the architectural considerations for distributed model training. The repository also includes utility scripts to convert the book's content into a single, standardized Markdown file or a Word document for offline reading.

Who it’s for

This resource is intended for researchers, engineers, and developers working with JAX and TPUs who need to understand the technical nuances of scaling LLM workloads.

Highlights

• Explains TPU architecture and operation.
• Guides users on choosing parallelism schemes to optimize performance.
• Provides tools to generate a single-file version of the book for portability.
• Authored by experts from Google DeepMind.