How Meta trains large language models at scale

This post details Meta's infrastructure innovations for training large language models (LLMs) at scale. It highlights the shift from training many small models to fewer, massive ones, and the resulting challenges in hardware reliability, fast recovery, state preservation, and GPU connectivity. Key infrastructure developments include optimizing hardware (e.g., modifying the Grand Teton platform with higher TDP GPUs and HBM3), data center deployment strategies for high compute density, and network fabric choices (RoCE and InfiniBand). The post elaborates on network optimizations for LLM training, including assigning communication patterns to network topology, implementing topology-aware collective communication algorithms (e.g., recursive doubling/halving), and enhancing network load balancing and routing for fat flows. It also touches upon storage solutions and the importance of PyTorch and efficient scheduling.