Result in advance:
Cuda needs to be installed in addition to the display driver in various ways, Tensorflow needs the system install, Pytorch does not (unless you install it from source).
Mind that "CUDA Toolkit" (standalone) and cudatoolkit (conda) are different!
Details (only fyi):
Why not just testing an installation that needs cuda to find out. Going to https://pytorch.org/get-started/locally/, you get
conda install pytorch torchvision cudatoolkit=10.2 -c pytorch as the installation command in conda prompt.
It chooses to install version 10.2. It would not install cuda if that came with the display driver.
The installation then installs a cuda toolkit:
The following NEW packages will be INSTALLED:
Then we see that the cudatoolkit-10.2.89 | 317.2 MB is probably too large to be plausibly included in the display driver.
C:\Program Files (x86)\NVIDIA Corporation, there are only three cuda-named dll files of a few houndred KB.
The mentioned cuda 11.0 in the release notes is just giving us the support information, not the actual installation. I have had a look at the release notes as well. It lists cuda 11.0 under "Software Module Versions", yes. Yet later under "New Features and Other Changes" it just says "Supports CUDA 11.0.", see https://us.download.nvidia.com/Windows/451.67/451.67-win10-win8-win7-release-notes.pdf.
- The accepted answer states that you need to install nvidia-cuda-toolkit to run the version commands at all (though referring not to Windows, but it is the same on Windows).
- The answer of using
nvidia-smi to get the version in the top right is rejected as wrong since it only shows which version is supported. It does not show if Cuda is actually installed. @BruceYo comments: [The command nvidia-smi] "will display CUDA Version even when no CUDA is installed."
This suggests again that cuda is not included in the display driver installation.