Reference Architecture for AI Training | CoreWeave Blog

This post defines a reference architecture for distributed AI training, detailing four interdependent layers: topology-aware orchestration, checkpoint-optimized storage, high-performance interconnect, and integrated observability. It contrasts this purpose-built architecture with general-purpose infrastructure, highlighting how the former addresses failure modes like GPU stragglers, hangs, and slow recovery. The post emphasizes that resilience must be designed from the start, not added later, and that a purpose-built architecture leads to measurable improvements in utilization, stalled jobs, and recovery time, citing ~2x faster large-model training and improved sustained effective throughput.