How to install GPU support in Model Builder

Learn how to install the GPU drivers to use your GPU with Model Builder.

Hardware requirements

  • At least one CUDA compatible GPU. For a list of compatible GPUs, see NVIDIA's guide.
  • At least 6GB of dedicated GPU memory.


Image classification only

  • NVIDIA developer account. If you don't have one, create a free account.
  • Install dependencies
    • Install CUDA v10.1. Make sure you install CUDA v10.1, not any other newer version.
    • Install cuDNN v7.6.4 for CUDA 10.1. You cannot have multiple versions of cuDNN installed. After downloading cuDNN v7.6.4 zip file and unpacking it, copy <CUDNN_zip_files_path>\cuda\bin\cudnn64_7.dll to <YOUR_DRIVE>\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\bin.


What if I don't have a GPU installed locally?

Deep learning scenarios tend to run faster on GPUs.

Some scenarios like image classification support training on Azure GPU VMs.

However, if local GPUs or Azure are not an option for you, these scenarios also run on CPU. Note however that training times are significantly longer.

How do I know what GPU I have?

  1. Right-click on desktop
  2. If you see "NVIDIA Control Panel" or "NVIDIA Display" in the pop-up window, you have an NVIDIA GPU
  3. Click on "NVIDIA Control Panel" or "NVIDIA Display" in the pop-up window
  4. Look at "Graphics Card Information"
  5. You will see the name of your NVIDIA GPU

I don't see NVIDIA Control Panel (or it fails to open) but I know I have an NVIDIA GPU.

  1. Open Device Manager
  2. Look at Display adapters
  3. Install appropriate driver for your GPU.

How do I see what version of CUDA I have?

  1. Open a PowerShell or command line window
  2. Type in nvcc --version

cuda is not available, please confirm you have a cuda-supported gpu

  1. Open the GeForce Experience app.
  2. The application should show installed and available driver updates. If you have trouble seeing updates, you can get the latest drivers from
  3. Install the latest drivers.