GPU Stragglers and Training Failures Explained

This post details the failure modes of stragglers, synchronization stalls, and stalled GPUs in large-scale distributed AI training. It explains how these issues lead to inefficient compute utilization and proposes practical metrics like goodput, learning velocity, and MFU to quantify business impact. The post emphasizes that traditional infrastructure monitoring is insufficient and highlights the need for architectural control across placement, networking, storage, scheduling, and observability to prevent coordination failures. It positions CoreWeave Cloud as a platform designed to mitigate these issues by integrating these layers to reduce variance and improve training performance.