Planning a GPU workload
A GPU configuration must fit the software as well as the dataset. Accelerator memory, number of devices, CPU, host RAM and storage throughput can each limit performance. A model that fits in memory for inference may need substantially more resources for training.
Include these details in your request
- Framework and version, driver or CUDA requirements, model size, numerical precision and expected batch size.
- Whether the workload is training, inference, rendering or another task, plus expected concurrency and run duration.
- Dataset size, where it will be stored, how it reaches the GPU instance and whether sensitive information is involved.
- Required GPU model and count, licence needs, region preference, backup requirements and acceptable startup or maintenance windows.
Ask for a representative workload test when practical. Do not infer dedicated GPU access, device partitioning or included software licences from the plan name alone; the final quote must identify the supplied accelerator and allocation model.
