Why Distributed Training Fails at Scale | CoreWeave Blog

What this post added

This post defines and elaborates on the challenges and failure modes of distributed training at scale. It identifies three primary failure categories: compute, coordination, and recovery. It also highlights the diagnostic challenge of NCCL errors masking underlying issues. The post uses data from Epoch AI and Meta FAIR to illustrate the scaling of training times, model parameters, and the inverse relationship between cluster size and MTTF. It also references a ByteDance case study on straggler impact. The post advocates for a system-level approach to building resiliency for large-scale distributed training.