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Buy hardware, colocate, or rent from a GPU cloud? Work the decision for a 512-GPU need.

Compare ownership, colocation and rental using the same useful work, full annual costs and realistic demand. Work a break-even from explicit assumptions.

Updated Sep 2026 · Learn how AI infrastructure works through explanations, worked examples and diagrams, then practise applying it to interview questions.

Compare ownership, colocation and rental using the same useful work, full annual costs and realistic demand. Work a break-even from explicit assumptions.

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The concepts behind this question

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🖧 Hardware & Cluster Build-Out

Colocation, Power Contracts and Site SelectionA suitable site must deliver the required rack power, cooling and service capacity. Contract terms and facility lead times shape both cost and deployment dates.

🖧 Hardware & Cluster Build-Out

The Bill of Materials for a Training ClusterA cluster bill of materials includes networking, storage, management, power, cooling and spares as well as GPUs. Size each from the workload and topology.

🧮 Napkin Math & Capacity🔒 Premium

TCO: Buy vs RentBuying and renting GPU capacity have different cost and availability risks. Compare them over a stated lifetime and demand pattern, including idle time and operating costs.

🖧 Hardware & Cluster Build-Out

Accelerator Selection: H100 to B300 and RTX PRO 6000Choose an accelerator by checking memory capacity, memory bandwidth and compute throughput separately. Each limits a different part of the workload.

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Next in this trackYou have exactly 1 MW of facility capacity. Maximize training throughput.Next in this trackWhat does nvidia-fabricmanager do, and what exactly breaks when it is not running?Next in this trackDesign the physical layer for a multi-tenant GPU cloud. What changes versus a single-tenant cluster?

FEDITOR'S NOTE

Look for comparable output, complete cost boundaries, contract-specific idle charges and sensitivity to demand.

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