# Starting a GPU project on Selectel: resource and access options

Start a Selectel GPU project by verifying the cloud configuration, dedicated PCI device, storage, Data Science image and an initial access test.

Author: Retrivium team
Updated: 2026-11-01
Canonical URL: https://retrivium.ai/journal/selectel-gpu-server-access-options/
Language: en

For GPU rental on Selectel, choose a cloud server with a specified accelerator rather than an ordinary CPU configuration with a similar name. The documentation describes GPUs as dedicated PCI devices within a cloud server. Define the complete resource and working environment before uploading a large dataset.

## Verify the product configuration

The GPU guide describes supported configurations and documented ways to add an accelerator during creation or to an existing server. It does not mean every VM size accepts any card without restrictions.

Record minimum VRAM, system RAM and data volume, then inspect the relevant pool's options. A documentation list is not current inventory. For several accelerators, check the actual arrangement and supported interconnect rather than treating combined memory as one automatically usable device.

## Decide whether a prepared image helps

The Data Science image includes research tools such as Jupyter. It can shorten preparation when its versions suit the project. A clean or custom OS provides a different workflow and needs its own compatibility work.

| Starting point | First check |
| --- | --- |
| Data Science environment | Included tools, versions and access |
| Custom image | Driver and reproducible dependencies |
| New GPU server | Full configuration and disk choice |
| Existing server change | Supported modification procedure |

A notebook being installed does not make a specific model ready. Inputs, parameters, permissions and preservation remain project responsibilities. Review applicable product access and ordering conditions for your location rather than assuming worldwide eligibility.

## Verify the normal access path

Establish the OS user, public SSH key and allowed network route. Console access and an operating-system login are separate capabilities. For a notebook, follow the product's documented route and retain authentication.

Test as the intended user, not just through an accidental administrator session. Check accelerator visibility, directory ownership and a small file write before loading the full dataset. That separates resource preparation from application errors.

## Run a bounded research pilot

A small image-processing test can establish setup time, a valid result and its actual saved location. Verify that a notebook and output can be restored from the saved copy before expanding the workload.

If dependency versions do not match, record the specific reason for a custom image. Finish with an exit procedure describing retained disks, result export and expense responsibility. Renting provider equipment and offering your own machine to an operator are different arrangements; the cloud pilot is useful evidence about the workload, not an equipment-supply programme at Selectel.

## Turn the check into a practical next step

Choose the confirmed GPU product and operating workflow, then verify access, device visibility and saved data on a small representative pilot.

For the documented details, consult the [primary documentation](https://docs.selectel.ru/cloud-servers/create/gpus/); [configuration reference](https://docs.selectel.ru/cloud-servers/applications/ml-ai/dsvm/). Continue with [selectel gpu jupyter notebook access](/journal/selectel-gpu-jupyter-notebook-access/) and [cloud gpu ephemeral vs persistent checklist](/journal/cloud-gpu-ephemeral-vs-persistent-checklist/).