---
title: "Connect to a Linux GPU node"
description: "SSH into a Linux GPU node the same way as any Linux node, then confirm the card is visible with nvidia-smi."
content_type: "how-to"
product: "gpu-node"
platform: "MyAccount"
url: "/myaccount/gpu-node/how-to/connect-to-a-linux-gpu-node/"
topic: ["create-and-connect"]
---

# Connect to a Linux GPU node

Connecting to a Linux GPU node is the same SSH flow as any Linux node. The only addition is
confirming the card once you are in; for the full connect reference, finding the IP, password
versus key, host fingerprints, see
[Connect to a Linux node](/myaccount/node/how-to/connect-to-a-linux-node/).

## Connect over SSH

```bash frame="none"
ssh <username>@<gpu-node-ip>
```

Or with an SSH key:

```bash frame="none"
chmod 600 ~/.ssh/<private-key-file>
ssh -i ~/.ssh/<private-key-file> <username>@<gpu-node-ip>
```

The default user, password retrieval, and security group rules are identical to a regular
Linux node.

## Check the GPU with nvidia-smi

Run `nvidia-smi` as your first action after login:

```bash frame="none"
nvidia-smi
```

A healthy node returns the card name, driver version, CUDA version, memory, and utilization. On
a single-card L4 node, the top of the output looks like this; your card, driver, and numbers
will differ:

```text
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 580.82.07              Driver Version: 580.82.07      CUDA Version: 13.0     |
|-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA L4                      Off |   00000000:00:06.0 Off |                    0 |
| N/A   34C    P8             12W /   72W |       0MiB /  23034MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
```

Here is what each part tells you:

| Field          | What it tells you                                                                          |
| -------------- | ------------------------------------------------------------------------------------------ |
| Driver Version | The installed NVIDIA datacenter driver.                                                    |
| CUDA Version   | The maximum CUDA the driver supports. Your workload's CUDA build must be at or below this. |
| Name           | The detected GPU model; confirm it matches the card you ordered.                           |
| Memory-Usage   | Idle memory should read near 0 MB on a fresh node.                                         |
| GPU-Util       | Should read `0%` on idle.                                                                  |
| Persistence-M  | `On` keeps the driver loaded between jobs, which suits long-running work.                  |

If `nvidia-smi` is not found, or reports it cannot communicate with the driver, see
[Troubleshooting GPU Node](/myaccount/gpu-node/troubleshooting/gpu-node/). Once the card checks
out, see [Run GPU workloads in Docker](/myaccount/gpu-node/how-to/run-gpu-workloads-in-docker/)
for the recommended way to run a real job, or
[Save and reuse a GPU image](/myaccount/gpu-node/how-to/save-and-reuse-a-gpu-image/) to skip
this setup on every new node.

## (Optional) Confirm the CUDA toolkit

If you compile CUDA code directly on the host rather than inside a container, confirm the
toolkit separately from the driver:

```bash frame="none"
nvcc --version
```

On container-based images, `nvcc` may exist only inside the container, not on the host; that is
expected, since the host only needs the driver.