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OP·Fredoline Eruo
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RUNLOCALAI · v38
Errors / Driver issues / Docker: could not select device driver "nvidia"
Driver issues

Docker: could not select device driver "nvidia"

docker: Error response from daemon: could not select device driver "nvidia" with capabilities: [[gpu]].
By Fredoline Eruo · Last verified Jun 12, 2026

Cause

Environment: Docker / Docker Desktop on Linux or WSL2 trying to run GPU containers (e.g. vLLM, Ollama, tgi).

Severity: high — GPU containers won't start.

  • nvidia-container-toolkit not installed
  • Toolkit installed but Docker daemon not restarted (config not picked up)
  • Docker rootless mode without the toolkit's rootless adjustments
  • WSL2 Docker Desktop with Windows host driver missing
  • docker run --gpus all used on a Docker version older than 19.03 (the flag didn't exist)

Solution

1. Install nvidia-container-toolkit (Ubuntu/Debian):

curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | \
  sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
distribution=$(. /etc/os-release; echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/libnvidia-container/$distribution/libnvidia-container.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 update && sudo apt install -y nvidia-container-toolkit

2. Configure Docker runtime + restart daemon:

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

3. Verify GPU is visible inside a container:

docker run --rm --gpus all nvidia/cuda:12.4.1-base-ubuntu22.04 nvidia-smi

4. Rootless Docker users: the toolkit needs an extra step:

nvidia-ctk runtime configure --runtime=docker --config=$HOME/.config/docker/daemon.json
systemctl --user restart docker

5. WSL2 Docker Desktop: install the toolkit inside the WSL distro, not on Windows. The Windows host driver passes through; you only need the Linux-side toolkit + Docker engine.

Alternative solutions

Caveat: --gpus is a Docker-CLI shorthand. With Docker Compose, switch to the deploy.resources.reservations.devices syntax. With podman, use --device nvidia.com/gpu=all and the CDI workflow instead.

Related errors

  • CUDA driver version is insufficient for CUDA runtime version
  • PyTorch CUDA error: driver version is insufficient for CUDA runtime
  • WSL2: nvidia-smi works but PyTorch sees no CUDA / libcuda.so missing
  • WSL2 GPU not detected — nvidia-smi missing or empty
  • nvidia-smi: command not found

Did this fix it?

If your case was different, email Contact support with what you saw and we'll update the page. If it worked but took different commands on your platform, we want to know that too.