Inside multi-node training: How to scale model training across GPU clusters
Together AI
The article explains how to train large foundation models across multiple GPU-connected machines using distributed training techniques like data parallelism, tensor parallelism, and pipeline parallelism. A 72B parameter model trained on 128 GPUs achieved approximately 2,500 tokens per second per GPU with 45-50% model flops utilization, while scaling from 8 to 128 GPUs can reduce training time from 30 days to 2-3 days. Proper multi-node training requires careful infrastructure setup, network optimization, fault tolerance mechanisms, and monitoring to maintain GPU utilization above 70% and handle the hardware failures that occur routinely in large clusters.
Why it matters
Learn how foundation models are trained at scale using multi-node GPU clusters, including distributed training techniques, infrastructure requirements, and practical steps to scale training efficiently.
