A new GPU generation is arriving. Plan the migration of a live 2,048-GPU cluster.

Almost nothing about a generation change is the GPU. The facility usually cannot take the new part in the old positions, the fabric generation may not match, and the two fleets have to coexist for months. The sequencing that avoids a capacity trough, and the decision people get wrong.

Updated Sep 2026 · Grounded in real AI infrastructure interview loops and written to a senior-engineer editorial bar, with every number worked and every diagram hand-built.

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The concepts behind this question

Ranked by how closely each one overlaps this question's topic, so the first card is the thing to read if the answer above moved too fast.

Foundational

🧮 Open Weights & Serving Engines

Capacity Planning for Open-Weights FleetsPlanning a fleet for a sparse open-weights model works differently from planning one for a dense model, because memory follows total parameters and throughput follows active parameters, and those now differ by more than twenty times. The sizing goes in one direction only: from a traffic forecast to tokens per second, to replicas at a measured operating point, to GPUs, to racks and kilowatts. Doing it in the other direction, from an available GPU count, produces a fleet that fits the hardware rather than the demand.

Foundational

🖧 Hardware & Cluster Build-Out

Colocation, Power Contracts and Site SelectionFor most organizations the constraint on deploying GPUs is not the GPUs. It is finding a hall that can deliver 100 kilowatts or more per rack, reject that heat with liquid, and sign a contract for the power years before the hardware exists. Colocation contracts price reserved capacity rather than consumption, cooling capability is what eliminates most sites, and the lead time on new electrical supply is measured in years while GPUs arrive in months.

Advanced

🧮 Napkin Math & Capacity🔒 Premium

Power and Datacenter ConstraintsThe binding constraint on new GPU capacity in 2026 is not chips or capital but megawatts: an H100 node draws about 10 kW, a GB200 NVL72 rack about 120 kW, and a 100,000-GPU cluster needs on the order of 150 MW with cooling. This page converts GPU counts to power, power to cooling and facility requirements, and both to cost, so a candidate can size a training hall from a power budget and explain why liquid cooling, PUE and the local grid decide where the next cluster goes.

Foundational

🧮 Napkin Math & Capacity

KV Cache SizingThe KV cache is the memory that decides how many users a serving replica can hold and how long their context can be. Its size per token comes from four numbers in the model's config file (layers, KV heads, head dimension, bytes per element) and one formula; multiplied by context and concurrency it is the number every capacity plan is built on. This page derives it, works it for four models including an MLA one, and shows the two places candidates get it wrong by a factor of eight.

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UP NEXT ON YOUR JOURNEY

Next in this trackA vendor claims their accelerator beats an H100 at half the price. How do you evaluate that?Next in this trackDesign the health check that decides whether a node may accept jobs.Next in this trackA node fails burn-in twice and passes on the third attempt. Do you accept it?

FEDITOR'S NOTE

Scored on facility capability as the first constraint, on managing a mixed fleet with jobs that cannot span generations, and on avoiding a capacity trough during the transition.

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