A single high-end GPU trains small models well. Modern AI — large language models, diffusion systems, recommendation engines — needs many GPUs working as one, connected by networking fast enough that the machines behave like a single computer.
The architecture in brief
Training workloads are split across GPUs, with gradients exchanged between nodes every step. The network carrying that exchange becomes the hidden bottleneck: once compute is plentiful, interconnect quality decides whether the cluster runs efficiently or idles.
What it means for planning
Clusters reward planning: matching GPU memory to model size, provisioning networking deliberately, and treating cooling and power as first-class requirements. Start with the workload profile, size the cluster to it, and scale in steps you can validate.
