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Build the schedule for a 2,048-GPU deployment. What is on the critical path?

The GPUs are almost never the critical path and planning backward from their delivery date is how deployments slip. The real ordering, what can run in parallel, the two dependencies that cannot, and the cost of a month of delay expressed in the unit that gets attention.

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.

The GPUs are almost never the critical path and planning backward from their delivery date is how deployments slip. The real ordering, what can run in parallel, the two dependencies that cannot, and the cost of a month of delay expressed in the unit that gets attention.

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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.

🖧 Hardware & Cluster Build-Out

Burn-In and Acceptance TestingNew hardware fails early or it fails late, and burn-in exists to move the early failures before the cluster is handed over rather than after. A proper acceptance test runs every layer under sustained load for days, compares every node against its siblings rather than against a specification, and produces a signed number the buyer and the vendor both agree on. The comparison is the important part: identical hardware running identical work should produce identical numbers, and the outliers are the finding.

🖧 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.

🖧 Hardware & Cluster Build-Out

The Bill of Materials for a Training ClusterA GPU cluster is not a pile of GPUs. A 512-GPU scalable unit built to NVIDIA's DGX SuperPOD B300 reference architecture needs 64 nodes, four separate networks, thousands of transceivers, storage that can absorb a checkpoint burst, a management plane, racks, power distribution and cooling equipment. Writing the list out in order is how a design becomes a purchase order, and the items people forget are the ones that hold up a deployment for weeks.

🖧 Hardware & Cluster Build-Out

Accelerator Selection: H100 to B300 and RTX PRO 6000Three published numbers decide which accelerator suits a workload, and they are independent: memory capacity gates what fits, memory bandwidth gates decode speed, and tensor FLOPS gate prefill and training. As of September 2026 the parts NVIDIA sells for datacenters span 80 GB to 288 GB and 1.6 TB/s to 8 TB/s, and the gap between the compute number and the bandwidth number has widened every generation, which is why a part that looks four times faster on a slide is often twice as fast on a decode workload.

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UP NEXT ON YOUR JOURNEY

Next in this trackHow many spare GPUs, nodes, cables and transceivers do you hold for a 2,048-GPU fleet?Next in this trackTwenty-eight of sixty-four new nodes fail acceptance. What do you do first?Next in this trackA GPU cluster has four separate networks. Name them and say what each one carries.

FEDITOR'S NOTE

Scored on identifying power and cooling as the critical path rather than compute, on what parallelizes and what does not, and on pricing the delay.

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