First, understand
what fits.
A workload has a shape. Memory, precision and batch size define it. Explore the constraints before choosing the machine.
Read the planning guide ↗Reference size at FP32
- Parameters at chosen precision
- GiB
- Activations × batch
- GiB
- Runtime allowance
- 2.0 GiB
- Available after reserve
- GiB
This example fits within the planning budget.
No magic score.
A visible calculation.
The result is a planning estimate for an illustrative workload. Actual peak usage depends on your model, execution strategy and software. Measure before you allocate.
See every assumption ↗Go beyond
the spec sheet.
Does more GPU memory always mean a faster job?+
No. Memory capacity determines what can fit, while execution time also depends on compute, bandwidth, software and data movement.
Can several GPUs replace one larger GPU?+
Only if the workload supports distribution. Communication and partitioning overhead must be measured for the intended execution strategy.
What should a benchmark report include?+
Workload, input shape, precision, environment, warm-up method, repeated measurements and whether data transfer is included.
Do the example capacities represent available inventory?+
No. They are illustrative planning examples. They do not represent an available inventory, price quotation or running customer job.