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Training Cluster Design at 10k GPUs
Design a cluster for training frontier models is the prompt that tests whether a candidate can hold hardware, network, storage, scheduling and reliability in one head at once. The answer is a bill of materials with a reason for every line: how many GPUs and why, how they are grouped into pods, how the fabric connects the pods and what it costs a collective to cross one, how much storage bandwidth the checkpoints and the data loader need, how power and cooling bound the whole thing, and how the failure statistics set the spare pool and the checkpoint cadence. This page derives each line for a 10,240-GPU cluster.
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AI Infrastructure System DesignDesign a training cluster for 10,000 GPUs: pods, fabric, storage, failure domains and the scheduler. Justify every number.→Hardware, Cabling & Cluster Build-OutYou have budget for roughly 1,000 GPUs and a greenfield site. Specify the cluster.→AI Infrastructure System DesignDesign a training cluster for a one-trillion-parameter MoE. Size it, choose the parallel layout, and map it onto the fabric.→Networking, Interconnects & StorageDesign the complete network for a 1,024-GPU cluster: compute fabric, storage, management. What are the counts and where does the money go?→Hardware, Cabling & Cluster Build-OutYou have exactly 1 MW of facility capacity. Maximize training throughput.→GPU & Accelerator ArchitectureDesign a non-blocking network fabric for 512 H100s. How many switches, how are they wired, and where does the NVLink domain end?→
