# Use your GPU in Docker containers: NVIDIA, AMD and Intel

> Give Docker containers access to the GPU: install the NVIDIA Container Toolkit, pass AMD devices /dev/kfd and /dev/dri, and reserve GPUs in Docker Compose.

Difficulty: Intermediate

Docker containers are isolated from the host's devices, so a GPU must be passed in on purpose. How depends on the GPU maker: **NVIDIA** uses the NVIDIA Container Toolkit, **AMD** and **Intel** GPUs are passed in as device files. This guide sets up both and shows the Compose syntax.

## Before you start

- A GPU server with a working driver: `nvidia-smi` (NVIDIA) or `rocminfo` (AMD) answers on the host; see [install NVIDIA drivers and CUDA](/guides/install-nvidia-driver-cuda) or [install AMD ROCm](/guides/install-amd-rocm).
- Docker Engine from Docker's repository: [install Docker on Ubuntu](/guides/install-docker-ubuntu).

## NVIDIA: install the NVIDIA Container Toolkit

Add NVIDIA's repository with its signing key, then install the toolkit:

```bash
curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
sudo apt-get update
sudo apt-get install -y nvidia-container-toolkit
```

NVIDIA's guide pins an exact version of the four toolkit packages; installing `nvidia-container-toolkit` without a version installs the current one with its dependencies.

Register the NVIDIA runtime with Docker and restart Docker:

```bash
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker
```

**Verify** with NVIDIA's sample workload:

```bash
sudo docker run --rm --runtime=nvidia --gpus all ubuntu nvidia-smi
```

The container prints the same `nvidia-smi` table as the host.

### NVIDIA GPUs in Docker Compose

Reserve the GPU in the service definition:

```yaml
services:
  app:
    image: ollama/ollama
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
```

Use `count: all` to give the service every GPU.

## AMD: pass the ROCm devices

AMD GPUs need two device paths: `/dev/kfd` (the compute interface) and `/dev/dri` (one node per GPU). AMD's example:

```bash
docker run -it --rm --device /dev/kfd --device /dev/dri --security-opt seccomp=unconfined rocm/rocm-terminal bash
```

Inside the container, `rocminfo` lists the GPU. `--security-opt seccomp=unconfined` is optional; AMD recommends it for HPC workloads that use `numactl`.

In Compose:

```yaml
services:
  app:
    image: rocm/rocm-terminal
    devices:
      - /dev/kfd
      - /dev/dri
```

## Intel: pass the render devices

Intel GPUs are reached through `/dev/dri`. Pass it in and use an image that contains Intel's compute or media runtime for your GPU:

```bash
docker run --rm --device /dev/dri ubuntu ls -l /dev/dri
```

## Troubleshooting

**`could not select device driver "" with capabilities: [[gpu]]`.** Docker does not know the NVIDIA runtime. Run `sudo nvidia-ctk runtime configure --runtime=docker` and restart Docker.

**`nvidia-container-cli: initialization error`.** The NVIDIA driver on the host is not working. Check `nvidia-smi` on the host first.

**AMD: `Permission denied` on `/dev/kfd`.** The user running the container's process lacks access to the devices. Run as root inside the container, or add the host's `video` and `render` group IDs with `--group-add`.

**The application in the container uses the CPU.** The image may not include GPU support, or it was started without the GPU flags. Check the image's documentation for the GPU tag or variant.

## Next steps

- Chat with local models on your GPU: [Open WebUI with Ollama](/guides/open-webui-ollama).
- Something does not work: [GPU troubleshooting](/guides/gpu-troubleshooting).

## Frequently asked questions

### Do I need the CUDA toolkit on the host?

No. The host needs the NVIDIA driver and the NVIDIA Container Toolkit; CUDA libraries come inside the container images.

### Can several containers share one GPU?

Yes. Several containers can use the same GPU at the same time; they share its memory, so make sure their models fit together.

### Why does the container say no GPU was found?

Usually the GPU was not requested: add --gpus all for NVIDIA, or the --device options for AMD and Intel, or the GPU reservation in Compose.

### Does this work with Podman?

The NVIDIA Container Toolkit also documents Podman through the Container Device Interface (CDI). The commands in this guide are for Docker Engine.

---

Source: <https://hyperdc.com/guides/products/gpu-in-docker-containers>\
Updated: 2026-10-15