Reference Architecture for AI Training | CoreWeave Blog

What this post added

This post introduces a reference architecture for distributed AI training that emphasizes a purpose-built, layered approach over general-purpose infrastructure. It details four key interdependent layers: topology-aware orchestration for optimized job placement and fault containment, checkpoint-optimized storage for frequent and high-throughput writes to minimize recovery loss, high-performance interconnects for efficient GPU communication and collective offload, and integrated observability for cross-layer visibility and proactive fault detection. The post argues that this integrated system design is crucial for achieving reliability and performance at production scale, contrasting it with fragmented, bolted-on solutions.