Dedicated GPU infrastructure

How to choose a GPU server rental

A useful rental request starts with the workload. This checklist helps our team match your requirements to a dedicated server without relying only on a GPU model name.

1. Describe the application

Include the framework, container image, model name and the type of work: inference, development, training or another GPU workload. For inference, include expected context length and simultaneous requests.

2. Separate GPU memory from system memory

Model weights are only part of GPU memory use. Runtime buffers, activations and inference cache also need space. System RAM serves a different purpose and does not automatically replace GPU memory.

3. Check multi-GPU support

Multiple GPUs require software that can use them. Communication between GPUs can affect performance, so ask about the proposed server configuration and validate your workload before planning capacity around theoretical numbers.

4. Include CPU, storage and network needs

Data loading and preprocessing may need substantial CPU and RAM. Specify dataset size, scratch space and persistence requirements. Include expected upload and download volume and whether you need multiple servers to communicate.

5. Agree on rental terms

Confirm the offered hardware, price, duration, access method and support arrangements with our team. Keep your own backups and confirm data retention expectations before ending a rental.