Open-source LLM training is a mess. Here is how it all works.

Navigating the extensive library landscape in the LLM training ecosystem can be daunting, as it lacks clear guidance on the interplay and relevance of different components. The author, who transitioned from Parsed to Baseten and serves as CTO, shares insights into the complexities of entering this field and provides an overview of the four-layer stack for modern open-source LLM training, which includes systems, core runtime, training, and inference. The post delves into various components like PyTorch, CUDA, NCCL, and scaling frameworks such as Megatron and DeepSpeed, highlighting their roles and interdependencies. It also discusses the distinctions and overlaps between training loops, orchestration tools, and inference engines like vLLM, SGLang, and TensorRT-LLM, emphasizing the evolving nature of these libraries. The author notes Baseten's approach to developing in-house solutions and the importance of a robust training stack to support diverse training techniques, while also acknowledging the challenges and opportunities in distributed training.