This repository offers a collection of eight challenging puzzles focused on the practicalities of training large language models (LLMs) across numerous GPUs. Aimed at researchers and engineers seeking hands-on experience with distributed training primitives, memory efficiency, and compute pipelining, it provides a unique learning opportunity for those interested in large-scale AI model development.

How It Works

The puzzles are designed to simulate real-world challenges encountered when scaling neural network training to thousands of GPUs. They focus on understanding and implementing key techniques for memory optimization and efficient parallel computation, enabling users to grasp the core concepts behind large-scale distributed deep learning.

Quick Start & Requirements

• Install/Run: Recommended to run in Google Colab. A link to a starter notebook is provided.
• Prerequisites: Google Colab environment.
• Links: Starter Notebook, Previous Puzzles

Highlighted Details

• Focuses on practical challenges of distributed LLM training.
• Emphasizes memory efficiency and compute pipelining.
• Part of a series of six related puzzle repositories by Sasha Rush.

Maintenance & Community

This project is maintained by Sasha Rush. Further community interaction details are not provided in the README.

Licensing & Compatibility

The repository does not explicitly state a license. Users should assume all rights are reserved or contact the author for clarification.

Limitations & Caveats

The puzzles are designed for educational purposes and may not cover all edge cases or advanced optimizations found in production-grade distributed training frameworks. The primary focus is on conceptual understanding rather than production-ready code.