Distributed training at scale fails in predictable ways. This technical guide breaks down why general-purpose cloud infrastructure falls short and how purpose-built reference architecture—across orchestration, storage, interconnect, and observability—reduces job failures and maximizes GPU utilization in production AI training environments.
- Understand the four failure types that compound as you scale to 1,000+ GPUs: compute, coordination, recovery, and observability.
- See how topology-aware orchestration, checkpoint-optimized storage, high-performance interconnect, and integrated observability work together as a system.
- Compare purpose-built versus general-purpose architecture across node fault detection, checkpoint frequency, cross-layer visibility, and more.
