Five reasons to own your inference instead of renting it.Rented tokens are a bill that never stops. Owned tokens get cheaper every quarter the server runs.
Here is when owning is worth it, and why it is easier than it used to be.
For regulated, classified, sovereign and clinical workloads this is a condition of operating. On-premises deployment removes the data-residency question from the architecture.
A rented token costs what it costs, priced to someone else’s margin. An owned server is an asset whose output grows while it depreciates, so unit cost falls as you use it more.
Hosted capacity tightens exactly when demand spikes. Capacity you own is there when traffic arrives, and every model has its own GPU, so one busy job never slows another.
Metered consumption is the hardest AI line to forecast. A known capital cost against known power and known throughput is a number finance can model and depreciate.
MI350P is an air-cooled PCIe card at 600 W, and a full 30-GPU system draws about 22 kW. No liquid cooling, no rack redesign, no new power distribution.