GPU Support in Docker Desktop for Windows

--- title: GPU support in Docker Desktop for Windows linkTitle: GPU support weight: 40 description: How to use GPU in Docker Desktop keywords: gpu, gpu support, nvidia, wsl2, docker desktop, windows toc_max: 3 aliases: - /desktop/gpu/ --- > [!NOTE] > > Currently GPU support in Docker Desktop is only available on Windows with the WSL2 backend. Docker Desktop for Windows supports NVIDIA GPU Paravirtualization (GPU-PV) on NVIDIA GPUs, allowing containers to access GPU resources for compute-intensive workloads like AI, machine learning, or video processing. ## Prerequisites To enable WSL 2 GPU Paravirtualization, you need: - A Windows machine with an NVIDIA GPU - Up to date Windows 10 or Windows 11 installation - [Up to date drivers](https://developer.nvidia.com/cuda/wsl) from NVIDIA supporting WSL 2 GPU Paravirtualization - The latest version of the WSL 2 Linux kernel. Use `wsl --update` on the command line - To make sure the [WSL 2 backend is turned on](wsl/_index.md#turn-on-docker-desktop-wsl-2) in Docker Desktop ## Validate GPU support To confirm GPU access is working inside Docker, run the following: ```console $ docker run --rm -it --gpus=all nvcr.io/nvidia/k8s/cuda-sample:nbody nbody -gpu -benchmark ``` This runs an n-body simulation benchmark on the GPU. The output will be similar to: ```console Run "nbody -benchmark [-numbodies=<numBodies>]" to measure performance. -fullscreen (run n-body simulation in fullscreen mode) -fp64 (use double precision floating point values for simulation) -hostmem (stores simulation data in host memory) -benchmark (run benchmark to measure performance) -numbodies=<N> (number of bodies (>= 1) to run in simulation) -device=<d> (where d=0,1,2.... for the CUDA device to use) -numdevices=<i> (where i=(number of CUDA devices > 0) to use for simulation) -compare (compares simulation results running once on the default GPU and once on the CPU) -cpu (run n-body simulation on the CPU) -tipsy=<file.bin> (load a tipsy model file for simulation) > NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled. > Windowed mode > Simulation data stored in video memory > Single precision floating point simulation > 1 Devices used for simulation MapSMtoCores for SM 7.5 is undefined. Default to use 64 Cores/SM GPU Device 0: "GeForce RTX 2060 with Max-Q Design" with compute capability 7.5 > Compute 7.5 CUDA device: [GeForce RTX 2060 with Max-Q Design] 30720 bodies, total time for 10 iterations: 69.280 ms = 136.219 billion interactions per second = 2724.379 single-precision GFLOP/s at 20 flops per interaction ``` ## Run a real-world model: Llama2 with Ollama Use the [official Ollama image](https://hub.docker.com/r/ollama/ollama) to run the Llama2 LLM with GPU acceleration: ```console $ docker run --gpus=all -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama ``` Then start the model: ```console $ docker exec -it ollama ollama run llama2 ```

Docker Desktop for Windows supports NVIDIA GPU Paravirtualization (GPU-PV) on NVIDIA GPUs, allowing containers to access GPU resources for compute-intensive workloads. This document outlines the prerequisites for enabling GPU support with WSL2 backend and provides instructions for validating GPU access inside Docker and running a real-world model (Llama2) with GPU acceleration.