GPU server rental: what to check before your first run

Check the hardware against the agreed configuration

Before running the job, verify GPU model and count, available memory, CPU, host RAM and free disk space. For multiple accelerators, establish the topology and interconnect. If your application needs specific GPU features, check the exact device variant rather than just the family name.

ASUS GX10 shares memory between CPU and GPU. For a two-node setup, check both systems and communication between them. Connecting two memory capacities does not create one pool; the application determines how the work is distributed.

Run a minimal example in the intended environment

Pin the Docker image version or package list, driver, runtime and launch command. First verify that the GPU is visible inside the working environment. Then load a small model or run a short operation using the same libraries and extensions as your main project.

For ARM64 systems, including GX10, check the architecture of the image and all binary dependencies. If the program builds CUDA extensions at first launch, allow time for compilation and storage for temporary files. Avoid changing several dependency versions at once when diagnosing a failure.

Agree on access and data handling

Confirm the connection method, user permissions, network restrictions and available storage. Prepare a separate public key for SSH. Do not put private keys or API tokens in the request description; use an agreed method to transfer temporary credentials.

Before uploading production data, agree on environment isolation, retention and file deletion. Use public or anonymized data for the first test. Keep copies of your code and important results outside the rented server.

Test workload limits and recovery

For inference, use the expected maximum context and concurrency. Measure peak memory, time to first token and generation speed. For training, run a short trial using realistic sequence lengths, then save a checkpoint and test resuming from it.

Define acceptance criteria in advance. A check that the process starts will not reveal failures that appear under sustained load or higher concurrency. Save the configuration and logs so you can reproduce results or investigate a problem.

Establish how the rental ends

Confirm the billable duration, extension procedure and when access ends. Export results before the session finishes, verify the copies and revoke temporary credentials. Extensions depend on confirmed hardware availability.

rentGPU lists configurations on request. The current website form is a demo and does not submit data. Preparing these requirements helps you review future rental terms; filling out the form does not create a reservation or establish server access.

Sources and documentation

NVIDIA container and framework compatibility

Keep exploring

Choosing a GPU for LLMs: RTX 4090, A100, H100 or GX10

GPU rental cost: how to estimate your budget

GPUs for LoRA and QLoRA: preparing to fine-tune an LLM

Preparing for your first run