docs: Remove maintainer CUDA builds knowledge base section

* In keeping with directing all CUDA build information to the cuda-feedstock
  user guides, remove almost all information from the CUDA builds section of
  the maintainer Knowledge Base and instead direct people to the user guides.
   - c.f. https://github.com/conda-forge/cuda-feedstock/blob/main/recipe/README.md

docs/maintainer/knowledge_base.md

@@ -2006,55 +2006,9 @@ if you're using a `c_stdlib_version` of `2.28`, set it to `alma8`.
 ## CUDA builds
 
 Although the provisioned CI machines do not feature a GPU, conda-forge does provide mechanisms
-to build CUDA-enabled packages. These mechanisms involve several packages:
-
-- `cudatoolkit`: The runtime libraries for the CUDA toolkit. This is what end-users will end
-  up installing next to your package.
-- `nvcc`: Nvidia's EULA does not allow the redistribution of compilers and drivers. Instead, we
-  provide a wrapper package that locates the CUDA installation in the system. The main role of this
-  package is to set some environment variables (`CUDA_HOME`, `CUDA_PATH`, `CFLAGS` and others),
-  as well as wrapping the real `nvcc` executable to set some extra command line arguments.
-
-In practice, to enable CUDA on your package, add `{{ compiler('cuda') }}` to the `build`
-section of your requirements and rerender. The matching `cudatoolkit` will be added to the `run`
-requirements automatically.
-
-On Linux, CMake users are required to use `${CMAKE_ARGS}` so CMake can find CUDA correctly. For example:
-
-```shell-session
-mkdir build && cd build
-cmake ${CMAKE_ARGS} ${SRC_DIR}
-make
-```
-
-:::note
-
-**How is CUDA provided at the system level?**
-
-- On Linux, Nvidia provides official Docker images, which we then
-  [adapt](https://github.com/conda-forge/docker-images) to conda-forge's needs.
-- On Windows, the compilers need to be installed for every CI run. This is done through the
-  [conda-forge-ci-setup](https://github.com/conda-forge/conda-forge-ci-setup-feedstock/) scripts.
-  Do note that the Nvidia executable won't install the drivers because no GPU is present in the machine.
-
-**How is cudatoolkit selected at install time?**
-
-Conda exposes the maximum CUDA version supported by the installed Nvidia drivers through a virtual package
-named `__cuda`. By default, `conda` will install the highest version available
-for the packages involved. To override this behaviour, you can define a `CONDA_OVERRIDE_CUDA` environment
-variable. More details in the
-[Conda docs](https://docs.conda.io/projects/conda/en/stable/user-guide/tasks/manage-virtual.html#overriding-detected-packages).
-
-Note that prior to v4.8.4, `__cuda` versions would not be part of the constraints, so you would always
-get the latest one, regardless the supported CUDA version.
-
-If for some reason you want to install a specific version, you can use:
-
-```default
-conda install your-gpu-package cudatoolkit=10.1
-```
-
-:::
+to build CUDA-enabled packages.
+See the [relevant CUDA user guides](https://github.com/conda-forge/cuda-feedstock/blob/main/recipe/README.md)
+for more information.
 
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