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

This post provides a detailed technical overview of the open-source LLM training ecosystem, mapping its four key layers: Systems (CUDA, cuBLAS, NCCL, NVLink, InfiniBand, Triton), Core Runtime (PyTorch, Safetensors), Training Stack (Transformers, Performance Primitives like FlashAttention, Transformer Engine), and Scaling Frameworks (Megatron, DeepSpeed, FSDP2). It explains the role and dependencies of each component, offering insights into how they fit together for reliable model training and deployment. The post highlights specific libraries and their contributions, such as FlashAttention's memory efficiency and Transformer Engine's FP8 mixed-precision training.