Training frontier foundation models and running distributed inference fleets requires clusters containing thousands or tens of thousands of high-performance GPUs (NVIDIA H100/H200/B200). In these environments, hardware represents tens or hundreds of millions of dollars in capital expenditure, where even a 5% drop in cluster utilization or communication efficiency translates to millions in wasted spend.
Traditional Kubernetes or CPU cluster schedulers (which schedule individual containers independently) fail catastrophically when applied to distributed AI workloads. If a distributed training job requires 64 GPUs across 8 nodes to run Ring-AllReduce, launching 7 nodes while waiting for the 8th creates a distributed deadlock: the 7 nodes sit completely idle, hoarding 56 GPUs while producing zero forward progress.
Furthermore, network topology cannot be treated as uniform. Intra-node NVLink delivers 900 GB/s to 1.8 TB/s of bisection bandwidth, while inter-node InfiniBand provides 400 Gbps to 800 Gbps. Placing ranks across fragmented switch fabrics introduces severe network bottlenecks, causing all nodes in the synchronous AllReduce step to stall at the speed of the slowest network hop.
This module deconstructs modern AI cluster orchestration: gang scheduling (All-or-Nothing), topology-aware placement algorithms, InfiniBand rail-optimized fabrics, GPU bin-packing and preemption, and distributed checkpoint-restart fault tolerance.