Estimate how many cloud GPUs instances are required to complete a target workload within a time window. The model combines measured throughput, expected availability, a capacity buffer, and billable runtime per instance. It rounds up to whole instances and shows total provisioned instance-hours, raw processing hours, and resulting productive headroom.
The main result is the minimum whole-instance count under the entered assumptions. Headroom is productive capacity beyond the original unbuffered demand.
Benchmark throughput with representative data. Provider quotas, startup time, dependencies, and job parallelism can impose additional limits.
For 100,000 work units at 500 units per instance-hour, a 24-hour window, 90% availability, and a 20% buffer, 12 instances are required when each may run 24 hours.
Capacity is shown as whole instances so the modeled workload can be completed rather than partially covered.
Use a benchmark from the same application, data shape, software stack, and instance configuration.
It reduces productive capacity for interruptions, downtime, startup delay, or other lost runtime.
Yes. Required workload is increased by the buffer, while each instance’s productive capacity is reduced by availability.
An instance may be permitted to run for less time than the overall completion window. The lower value controls capacity.
| Variable | Meaning |
|---|---|
| Demand | Useful output required by the deadline |
| Throughput | Useful work produced by one instance in one hour |
| Availability | Expected productive share of runtime |
| Buffer | Extra workload capacity added before sizing |