AI Infrastructure Readiness Checklist

Power

AI racks draw multiples of legacy rack power. This is where most projects stall.

We know our current available power capacity per rack, measured — not assumed The site can deliver 30 kW+ per rack (or we have a committed upgrade path) Power redundancy (N+1 or better) covers the planned AI load, not just today's We have engaged the utility / facility owner about incremental supply and timelines

Cooling

High ambient temperatures across the region make this a first-order constraint, not a detail.

Current cooling headroom is measured against the planned GPU load We have evaluated liquid cooling options for dense training clusters Hot/cold aisle containment (or equivalent airflow design) is in place or planned Cooling failure scenarios have defined automated responses

Network

Training clusters live or die on east-west bandwidth between GPUs.

We know the interconnect bandwidth our workloads need (training vs inference differ sharply) The fabric design supports high-bandwidth GPU-to-GPU traffic without oversubscription hot spots Storage network throughput can feed the GPUs at training data rates External connectivity (data ingress, user access) is sized for the use case

Storage

Idle GPUs waiting on data are the most expensive idle asset you can own.

Training data volumes are quantified, with growth projected We have a tiering plan: hot (training), warm (datasets), cold (archive) Storage throughput is benchmarked against GPU consumption rates for our workloads Data governance (residency, privacy, sovereignty) requirements are documented

Skills & Operations

The hardware is the easy part. Running it well is the differentiator.

Someone on the team has operated GPU infrastructure (or training is budgeted) Cluster scheduling / orchestration tooling is chosen, not "to be decided" Monitoring covers GPU utilization — so we'll know if we bought too much or too little There is a named owner for capacity planning and vendor management

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