Introduction

On Windows with Anaconda installed, "CUDA version" can mean several different things: the NVIDIA driver-supported CUDA version, the CUDA toolkit installed on the machine, or the CUDA runtime packaged inside a conda environment. CuDNN adds another layer because it may be installed system-wide or bundled through a framework package. To debug GPU issues correctly, you need to check all of these in the right place.

Start by Separating Driver, Toolkit, and Environment

These are not the same:

• the GPU driver reports what CUDA generation it supports
• the CUDA toolkit provides tools such as nvcc
• your conda environment may ship its own CUDA runtime libraries
• CuDNN may come from a framework package, not from a global install

If you mix those concepts, version checks become misleading very quickly.

Check the Driver-Level CUDA Information

Open Anaconda Prompt or a normal terminal and run:

nvidia-smi

This tells you whether the GPU driver is visible and usually shows the driver-reported CUDA compatibility level. That does not guarantee that the CUDA toolkit or CuDNN package is installed in the current conda environment.

It only tells you what the driver stack supports.

Check Whether the CUDA Toolkit Is Installed

If the CUDA toolkit is installed and on your PATH, this usually works:

nvcc --version

If nvcc is not found, that does not automatically mean CUDA is unusable inside Python. Many conda-based deep-learning setups do not rely on a system-wide nvcc at all.

That is why you should always inspect the active conda environment separately.

Inspect the Active Conda Environment

Activate the target environment first:

conda activate myenv
conda list

Then filter for GPU-related packages:

conda list cudnn
conda list cuda

If those packages are installed in the environment, this is often the most relevant version information for Python frameworks running from that environment.

Check From Python Directly

Framework-level checks are often the most useful because they tell you what the code can actually see.

For TensorFlow:

1import tensorflow as tf
2
3print("Built with CUDA:", tf.test.is_built_with_cuda())
4print("GPUs:", tf.config.list_physical_devices("GPU"))

For PyTorch:

1import torch
2
3print("Torch CUDA available:", torch.cuda.is_available())
4print("Torch CUDA version:", torch.version.cuda)
5print("CuDNN version:", torch.backends.cudnn.version())

These checks are often more actionable than system commands because they reflect the runtime that your actual code is using.

Where CuDNN Usually Shows Up on Windows

If CuDNN was installed manually with the CUDA toolkit, you may also find DLL files under your CUDA installation path. If it was installed through conda or bundled with the framework, the relevant files may live under the environment directory instead.

That is why "find the DLL on disk" can help, but it is not the most reliable first check for application debugging.

A Practical Order for Troubleshooting

When GPU code is failing, use this order:

1. nvidia-smi
2. conda activate ...
3. conda list cudnn
4. framework-level Python check

That sequence tells you whether the failure is at driver level, environment level, or framework level.

Common Pitfalls

• Treating the CUDA version shown by nvidia-smi as if it were the same as the toolkit version.
• Assuming missing nvcc means CUDA support is unavailable inside the conda environment.
• Checking one conda environment and then running code in another.
• Looking only at system-level installation and ignoring what the Python framework actually reports.
• Mixing driver, toolkit, runtime, and CuDNN versions into one vague "CUDA version" label.

Summary

• On Windows with Anaconda, CUDA and CuDNN version checks depend on which layer you mean.
• Use nvidia-smi for driver-level information.
• Use nvcc --version only for toolkit detection.
• Use conda list and Python framework checks for the active environment.
• Framework-level checks are usually the most relevant for real debugging.