Llm.c – LLM training in simple, pure C/CUDA (github.com)

Article summary

The article introduces llm.c, a project that aims to train large language models (LLMs) in simple, pure C/CUDA, without relying on PyTorch or cPython. The project provides a reference implementation in C and CUDA, allowing for pretraining and reproducing GPT-2 and GPT-3 models. The goal is to create a fast, efficient, and educational platform for LLM training. The project also includes a library of manually written kernels for various layers, which can be used for educational purposes or to achieve expert-level performance.

Main themes

• LLM training
• C/CUDA implementation
• Efficient computing
• PyTorch alternatives
• GPU acceleration
• Open-source development

What commenters say

• The overhead of Python is not significant in LLM training, and the language's convenience is more important for rapid development and experimentation.
• The use of optimized low-level code, such as CUDA, can greatly improve performance, but may also introduce complexity and dependencies.
• The size of CUDA is a significant issue, and efforts to slim it down or create alternative frameworks are necessary.
• There are existing LLMs trained without CUDA, and support for AMD and Intel hardware is growing, with some open-source projects and initiatives underway.
• The complexity of implementation, rather than the language itself, is the main bottleneck in achieving efficient LLM training.
• Some commenters question the need for optimizing install size, arguing that runtime overhead and memory usage are more important considerations.
• Others argue that using an agnostic framework instead of CUDA could be a better approach, allowing for more flexibility and portability.
• The development of alternative frameworks and libraries, such as tinygrad, is seen as a potential solution to the dominance of CUDA in AI computing.