Scaling Model Training with Multi-Node GPU Clusters

AI Impact Summary

This document explains how to scale model training across GPU clusters using multi-node training. The core challenge is training foundational models with trillions of parameters, which is impossible on a single GPU. Multi-node training allows for distributed training across hundreds or thousands of GPUs, significantly reducing training time and enabling the development of larger, more complex models. The document details techniques like data parallelism, tensor and pipeline model parallelism, and the importance of network interconnects (NVLink, InfiniBand) for efficient communication between GPUs.