Should you buy or rent a GPU for AI?
The honest answer is a break-even calculation. Renting wins for bursty, short, or uncertain workloads; buying wins only at sustained high utilization — and even then, only after you account for power, cooling, networking, and the gear going obsolete.
At today's cheapest cloud H100 rate of $3.85/GPU-hr, a $27.5K H100 pays for itself only after about 7,143 GPU-hours — roughly 10 months of 24/7 use, before power and hosting. Below that, renting is cheaper.
Illustrative: hardware-only, excludes ~$200–500/mo power+cooling per GPU, networking, and depreciation. See the live calculator for exact rental totals.
When renting wins
- Utilization under ~60–70% — you don't pay for idle time
- Short projects, experiments, or spiky training runs
- You need the newest silicon (H200/B200) without a capex cycle
- You want multi-region, managed networking, or instant scale-out
When buying can win
- Sustained near-24/7 use for 1–2+ years on the same generation
- You have cheap power, space, and someone to run the hardware
- Data-residency or air-gap requirements rule out cloud
- Smaller cards (RTX 4090/6000) for local dev — these pay back fast
If you decide to buy
Street prices for the common AI accelerators (affiliate links — see disclosure):
Or just rent — compare live rates
For most teams, renting is the right call. See current per-GPU-hour prices across 7 clouds: