GPU Cluster Requirements: The Planning Checklist Before You Buy

GPU clusters fail in predictable ways: the network that cannot carry the collectives, the storage that bottlenecks checkpointing, the rack power that arrives six months after the servers. Every one of those failures is a requirements gap that a disciplined checklist would have caught before a contract was signed. This page is that checklist — five pillars, each with its derivation rule from your workloads and its acceptance test — written for the procurement moment when you know you need capacity but have not yet specified it.

Prerequisites: Workload Definitions Before Specifications

Define the workloads first — training, fine-tuning, or inference, with model sizes, concurrency targets, and growth expectations — because every pillar's specification derives from these definitions, and clusters specified before workloads are guesses with invoices.

The workload-definition template, completed per workload:

Two disciplines make the template real: version it like code (workload definitions drift, and the pillars should re-derive on material change), and write down the mix (a cluster serving one workload optimizes differently than one serving a mix — the mix is a requirement, not an accident). Selection guidance across the industry agrees on this sequencing: use case first, then requirements.

The Three Technology Pillars: Compute, Network, Storage

Derive each pillar from the workload definitions: compute from model-memory and throughput arithmetic, network from communication patterns (collective-heavy training wants high-bandwidth fabrics), and storage from checkpoint bandwidth and dataset streaming rates — with a verification benchmark per pillar before acceptance.

The pillars interact, which is why each has an acceptance benchmark instead of a checkbox: fast compute exposes slow storage, fat fabrics expose thin storage paths, and any pillar specified in isolation will be wrong at the interfaces. Acceptance-test the combination, not the parts — and run the tests at the scale you bought, because collective behavior at 8 nodes tells you little about 64.

The Facility Pillar: Power Before Compute

Plan power availability before compute availability: rack power density, cooling capacity, and space determine what hardware can actually run, and datacenter guidance is explicit that facility constraints discovered after procurement invalidate the compute plan.

The facility checklist, in dependency order:

1. Compute rack power density from the intended hardware: GPU servers concentrate power in ways general racks were never designed for, and the arithmetic (servers per rack times nameplate draw) must be done before placement exists.
2. Cooling capacity matched to that density: air cooling has ceilings; beyond them the conversation becomes liquid, with its own facility requirements and lead times.
3. Space and rack count for the fleet plus its storage and management nodes, with expansion room for the growth expectation from your workload definitions.
4. Delivery timelines for all of it: facility work has the longest lead time in the entire project — power upgrades can outlast everything else combined.

This is the pillar renting removes: acquiring capacity as a service shifts facility planning to the provider, which is a legitimate strategy — but only if the provider's facility evidence replaces your own in the verification stage. The operating-model choice (own, colocate, dedicated provider) belongs in the requirements document explicitly, not implicitly by omission.

Verification: Test Every Pillar Before Acceptance

Verify each pillar with its benchmark at acceptance: collective-communication tests for fabric, checkpoint-write tests for storage, sustained-training and serving tests for compute, and measured power draw for facility — with the requirements document, not the vendor proposal, as the pass criteria.

Two rules make acceptance meaningful: the tests must run representative workloads (synthetic-only tests pass systems that fail in production shape), and defects found at acceptance are contract items, not favors — which is only true if the requirements document existed before the contract. When the operating model points to dedicated provider environments such as OneSource Cloud's private AI infrastructure, the same table governs, with provider evidence replacing your own facility column and the technology pillars verified on their hardware.

FAQ

Which pillar should we plan first?

Two in parallel: workload definitions (which drive every technology spec) and facility power (whose lead times outlast everything else) — start both on day one, because a facility surprise late in procurement is the error no budget fixes quickly.

Does renting change the requirements checklist?

It removes the facility pillar and converts capex to opex but keeps every technology pillar — compute, network, storage, and their acceptance tests — because rented clusters fail the same workloads the same ways; the checklist simply shifts verification from your facility to the provider's evidence.

How do we know the checklist is complete enough to approach vendors?

When each pillar has a derived spec, a verification method, and an owner — and the workload definitions behind them are written down; vendors fill gaps in implementation, not in requirements, and any question a vendor asks that your document cannot answer marks the gap to close first.