The decision to buy or rent GPU hardware for machine learning is often framed by a simplistic calculation:
$$\text{Break-Even Hours} = \frac{\text{Retail Purchase Price}}{\text{Hourly Rental Rate}}$$
If an enterprise accelerator or workstation card costs $15,000 to buy and rents for $2.50 per hour on the cloud, teams conclude: "If we run it for more than 6,000 hours (about 8 months of continuous use), buying is cheaper."
That calculation leaves out major cost factors.
When you purchase physical compute, the GPU on the invoice is only the first cost. Once you add power delivery, facility cooling, server chassis integration, annual hardware depreciation, and operational engineering overhead, the true break-even line changes significantly.
The Realistic 3-Year Total Cost of Ownership (TCO)
A reliable cost model for owning on-premise hardware includes four additional cost categories:
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