Building a TCO Model for an On-Prem AI Cluster, Line by Line

A total cost of ownership model is only as honest as the lines you put in it. Most first drafts capture the GPUs and stop there — which makes owned infrastructure look cheaper than it is and a later budget overrun inevitable. A credible TCO model spans capital and operating costs across a realistic three-to-five-year life. Here is what belongs in it.

Capital costs are more than GPUs

The accelerators are the headline, but a working node also needs host CPUs, system memory, NVMe storage, the chassis and power supplies, network interface cards, and the switch fabric that ties nodes together. At cluster scale, InfiniBand switches and cabling are a meaningful line of their own. Add racks, PDUs, and any structured cabling. Capture installation and configuration labor here too — it is real work whether you pay a vendor or your own staff.

The operating costs people forget

• Power: a dense GPU node can draw 6-10 kW continuously; multiply by your local cost per kWh and by the hours in your life horizon.
• Cooling: budget roughly an additional 30-50% of IT power for cooling overhead, captured by your facility's PUE.
• Facility or colocation: rack space, power circuits, and cross-connects if you are not using owned space.
• Support and warranty: extended coverage, spares, and the response-time tier you choose.
• Staff: the fraction of engineering and operations time spent keeping the cluster healthy.
• Software: licenses, scheduler, monitoring, and any managed platform layer.

Account for utilization and depreciation

A TCO number is meaningless without a utilization assumption. Cost per useful GPU-hour, not cost per GPU, is what you compare against cloud. A cluster at 40% utilization has more than double the effective cost of one at 85%. Pair this with a depreciation schedule — three to five years is typical for GPU hardware — and a residual value estimate if you expect to resell or redeploy at end of life.

Model power as a major variable

Over a multi-year horizon, electricity is frequently the second-largest line after the hardware itself. A 100-GPU cluster drawing roughly 0.8-1.0 MW including cooling, running continuously, consumes power on a scale that can rival a meaningful share of the capital cost across its life. If your TCO model treats power as a footnote, it is wrong. Run it at your real local rate, and run a sensitivity case at a higher rate.

Compare against cloud on the same terms

To compare fairly, build the cloud alternative with the same utilization and horizon — including egress, storage, reserved-instance discounts, and the support tier you would actually buy. The comparison that matters is total three-year cost per useful GPU-hour, owned versus rented, under your real usage. For sustained workloads the owned model usually wins; the TCO model is how you prove it to finance.

Nexus Compute supplies the configured, tested, warranty-backed hardware at the center of that model, and provides itemized quotes within 48 business hours so your capital and support lines reflect real specifications rather than estimates.