Start with the workload
Sizing begins with the model, the volume, and the latency you need. Without those numbers, any hardware estimate is a guess.
GPUs, servers, and utilization
Match hardware to sustained use, not a worst case that rarely happens. Idle GPUs are expensive. Plan for realistic utilization.
Owned vs cloud
Owning hardware can make sense at steady high volume. Below that, cloud or hosted inference is often cheaper once you include power, operations, and maintenance.
When not to buy
If the workload is small, spiky, or still changing, buying hardware early locks in cost and risk. Renting keeps you flexible.
When this matters
- You are about to spend on GPUs or servers.
- A vendor quote feels larger than your workload.
- Volume is steady enough to consider owning.
What to avoid
- Sizing for a worst case that never arrives.
- Ignoring power, operations, and maintenance in the cost.
- Buying before the workload is understood.
- Letting a vendor define your specs.