Inside multi-node training: How to scale model training across GPU clusters

This post details the infrastructure, techniques, and practical steps for scaling model training across GPU clusters, focusing on multi-node distributed training. It explains parallelism strategies (data, tensor, pipeline), network interconnects (NVLink, InfiniBand), and checkpointing/fault tolerance. A production example of training Qwen2.5-72B is provided, along with guidance on getting started, infrastructure verification, framework configuration, checkpointing implementation, scaling tests, and monitoring. It also covers common failure modes and troubleshooting for multi-node training.