An AI team runs a large language model training workload that consumes a consistent, predictable level of GPU compute every day of the year. Which financial characteristic of on-premise infrastructure makes it the more cost-effective choice for this scenario?
- AOn-premise hardware is purchased as a capital expenditure, so high steady-state utilisation spreads the fixed cost over time, yielding a lower effective cost per GPU-hour than cloud rental. Correct
- BOn-premise infrastructure is billed as an operational expenditure, so the team avoids large upfront payments and benefits from elastic scaling during the steady workload.
- CCloud providers charge a flat annual fee for reserved GPU instances, making cloud equivalent to on-premise capex and removing any cost advantage for steady workloads.
- DOn-premise infrastructure incurs zero operational costs because the hardware is owned outright, so the total cost of ownership is simply the purchase price.
Why A is correct: When utilisation is consistently high and predictable, the upfront capital expenditure amortises across every workload hour, undercutting the per-hour cloud rate over a multi-year horizon.
Why B is wrong: On-premise hardware is a capital expenditure, not an operational one. Describing it as opex conflates the financial model with cloud billing, which is the opposite arrangement.
Why C is wrong: Reserved cloud instances do reduce per-hour rates, but they are still opex payments and typically remain more expensive than owned hardware at very high steady-state utilisation, so the premise is incorrect.
Why D is wrong: Owned hardware carries ongoing operational costs including power, cooling, networking, maintenance, and staff, so zero operational cost is factually wrong and overstates the capex advantage.