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

What this post added

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.