Why Distributed Training Fails at Scale | CoreWeave Blog

This post details the common failure modes in large-scale distributed training (compute failures, coordination failures, recovery failures) and the challenges in diagnosing them, particularly the prevalence of NCCL errors as surface symptoms. It highlights the significant impact of straggler nodes on job reliability and compute utilization, citing Meta FAIR research showing drastically reduced MTTF with increasing GPU cluster size and a ByteDance study indicating substantial GPU-hours wasted due to stragglers. The post emphasizes the need for infrastructure resiliency and deep observability to correlate telemetry with application behavior for accurate failure diagnosis.