Prerequisites
- A known model or application
- Target precision and batch size
- A representative test workload
Security note
The current AIHOSTON public plan feed lists an NVIDIA A10 configuration. Confirm exact VRAM, driver, CUDA support, location, stock, and virtualization mode in writing before purchase.
Step 1
Identify the dominant memory users
Account for model weights, optimizer state, activations, input and output buffers, framework workspace, display use, and concurrent processes.
Step 2
Choose precision deliberately
Lower precision can reduce memory use, but compatibility and output quality depend on the model, framework, and hardware. Validate rather than assuming.
Step 3
Include batch and context size
Training batch size and inference context or sequence length can change activation and cache requirements substantially.
Step 4
Reserve operational headroom
Do not size to the theoretical byte. Leave capacity for framework allocation behavior, drivers, monitoring, and workload variation.
Step 5
Run a representative test
Measure peak allocated and reserved VRAM with the real framework, model, precision, concurrency, and input shape.
Step 6
Confirm the purchasable configuration
Compare the measured requirement with the live checkout specification. Ask sales to confirm any requirement that is not stated publicly.
Troubleshooting
- The workload reports out of memory
- Reduce batch, context, resolution, concurrency, or memory-heavy features; use a supported lower precision; or select a configuration with more verified VRAM.
- Reported free memory changes
- Check other processes, framework caches, driver overhead, and whether the GPU is shared or passed through as expected.
Frequently asked questions
Can model file size predict VRAM exactly?
No. Runtime memory also includes activations, caches, workspace, framework overhead, and concurrent work.