Training a large language model isn't just about GPUs crunching numbers - it's about orchestrating an entire distributed system. Before a single gradient is computed, hundreds of processes must discover each other, coordinate data access, synchronize updates, recover from failures, and keep expensive hardware fed with data. This article explores the hidden machinery behind distributed LLM training, from why 70B models don't fit on a single GPU to how PyTorch, Ray, samplers, networking, and check...