> ## Documentation Index
> Fetch the complete documentation index at: https://docs.nebius.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Getting started with Serverless AI jobs: Run nvidia-smi within a job
You can use Serverless AI jobs to work with AI models and perform such operations as fine-tuning, scientific simulations, data processing or batch inference.
To get started with jobs, use the example below. It demonstrates how to run `nvidia-smi` in a Serverless AI job. As a result, you receive information about the GPUs of the [container over a virtual machine](/compute/virtual-machines/containers) in which the job is running. In the web console, **Quick start Serverless job** runs the same kind of workload with settings already filled in.
## Prerequisites
The preparations for this guide depend on your preferred interface.
* Make sure you are in a [group](/iam/authorization/groups/index) that has the `admin` role within your tenant; for example, the default `admins` group. You can check this in the [Administration → IAM](https://console.nebius.com/iam) section of the web console.
* On the [Administration → Limits → Quotas](https://console.nebius.com/quota) page of the web console, check the **Number of virtual machines** (VMs) quota, under **Compute**: it should have at least one VM available. If necessary, [increase the quota](/overview/quotas#change-quotas).
* Make sure you are in a [group](/iam/authorization/groups/index) that has the `admin` role within your tenant; for example, the default `admins` group. You can check this in the [Administration → IAM](https://console.nebius.com/iam) section of the web console.
* On the [Administration → Limits → Quotas](https://console.nebius.com/quota) page of the web console, check the **Number of virtual machines** (VMs) quota, under **Compute**: it should have at least one VM available. If necessary, [increase the quota](/overview/quotas#change-quotas).
* [Install](/cli/install) and [configure](/cli/configure) the Nebius AI Cloud CLI.
Check that your project ID is saved in the Nebius AI Cloud CLI profile configuration:
```bash theme={null}
cat ~/.nebius/config.yaml
```
## Steps
### Run a Serverless AI job
1. In the sidebar, go to **AI Services** → **Jobs**.
2. Click **Quick start Serverless job**. The flow uses a verified container image and prefilled job settings.
3. Click **Create job**.
1. Get the ID of the default subnet where you create a job:
```bash theme={null}
nebius vpc subnet get-by-name --name default-subnet \
--format jsonpath='{.metadata.id}'
```
2. Create a job:
```bash theme={null}
nebius ai job create \
--name my-job \
--image nvidia/cuda:13.1.1-runtime-ubuntu24.04 \
--container-command bash \
--args "-c nvidia-smi" \
--platform gpu-l40s-a \
--preset 1gpu-8vcpu-32gb \
--timeout 1h \
--subnet-id
```
The command contains the following parameters:
* `--name`: Job name.
* `--image`: Container image to run. In the given example, the [nvidia/cuda:13.1.1-runtime-ubuntu24.04](https://hub.docker.com/layers/nvidia/cuda/13.1.1-runtime-ubuntu24.04/images/sha256-7ced05425d3bdea47c018551ee001f28081dd409efe1283872cd968ad3c1e957) is used. It contains the Ubuntu 24.04 operating system as well as drivers and other components for GPUs.
* `--container-command`: Terminal shell for commands to run.
* `--args`: Arguments for `docker run` to pass to the entrypoint command. The specified arguments require the container to run `nvidia-smi`.
* `--platform`: VM platform for the job. As Serverless AI jobs are based on containers over VMs, every job uses [Compute platforms and presets](/compute/virtual-machines/types).
* `--preset`: Number of GPUs, vCPUs and RAM allocated to the container. The preset must match the selected platform.
* `--timeout`: Duration after which the job is canceled if it has not completed.
* `--subnet-id`: Subnet ID.
The job takes several minutes to complete. While the job is running, you can check its status and logs.
### Check the job results
1. In the sidebar, go to **AI Services** → **Jobs**.
2. Next to the job, click **View logs**. Alternatively, select the job that you want to view the logs for and switch to the **Logs** tab.
1. Save the job ID to an environment variable:
```bash theme={null}
export JOB_ID=$(nebius ai job get-by-name --name my-job \
--format jsonpath='{.metadata.id}')
```
2. View information about the job:
```bash theme={null}
nebius ai job get $JOB_ID
```
The command output contains such data as the job status, start and finish times.
3. If the output doesn't indicate that the job has started, wait a few seconds. Next, run the previous command again and check that the job is running now.
4. When the job has started, check its logs:
```bash theme={null}
nebius ai job logs $JOB_ID
```
The logs contain information about the GPUs that `nvidia-smi` provides (in other words, the output of the `nvidia-smi` command).
You can run `nebius ai job get $JOB_ID` and `nebius ai job logs $JOB_ID` multiple times to determine whether the job is complete, and the full output of `nvidia-smi` is returned in logs.
### (Optional) Delete the job
When the job is complete, Serverless AI automatically releases all allocated computing resources and the container disk of the job. When you delete a complete job, you remove it from the list of Serverless AI jobs.
If you delete a running job, all the resources are released as well.
1. In the sidebar, go to **AI Services** → **Jobs**.
2. Locate the job and then click → **Delete**.
3. In the window that opens, confirm deleting the job.
To delete the job, run the following command:
```bash theme={null}
nebius ai job delete $JOB_ID
```