GPU Stragglers and Training Failures Explained

What this post added

This post details the common failure modes in large-scale AI training: stragglers, synchronization stalls, and stalled GPUs. It explains how these issues manifest as quiet failures (allocated but not utilized compute) and discusses their root causes, such as degraded hardware, network variability, and data pipeline bottlenecks. The post emphasizes the importance of moving beyond generic cluster health monitoring to per-rank metrics, communication vs. compute time, and I/O latency. It advocates for an architectural approach to prevent these coordination failures, highlighting CoreWeave Cloud's integrated infrastructure, orchestration, and observability as key to reducing variance and improving training reliability and efficiency.