Introduction

Installing cuDNN on a GCP Linux instance is mostly a Linux and NVIDIA packaging problem, not a GCP-specific one. The tricky part is that NVIDIA downloads are often gated by login and license acceptance, so a plain anonymous wget from a random cuDNN URL is usually not the smooth path people expect. In practice, the reliable options are using NVIDIA's package repository, copying a manually downloaded archive to the instance, or starting from a prebuilt GPU image.

Check the prerequisites first

Before thinking about cuDNN itself, verify that the instance has the basics:

• A compatible NVIDIA GPU
• An installed NVIDIA driver
• A compatible CUDA toolkit version
• Sufficient permissions to install system libraries

If those pieces are wrong, cuDNN installation is not the first problem to solve.

The practical path: download locally, copy to GCP, install there

For many developers, the least fragile workflow is:

1. Download the cuDNN archive from the NVIDIA developer site in a browser where you can log in and accept the license.
2. Copy that archive to the GCP instance.
3. Install it on the instance.

Copy the archive with gcloud compute scp:

1gcloud compute scp \
2  cudnn-linux-x86_64-archive.tar.xz \
3  my-instance:~ \
4  --zone us-central1-a

Then SSH into the instance and install it:

1gcloud compute ssh my-instance --zone us-central1-a
2
3tar -xf cudnn-linux-x86_64-archive.tar.xz
4cd cudnn-linux-x86_64-archive
5sudo cp include/cudnn*.h /usr/local/cuda/include/
6sudo cp lib/libcudnn* /usr/local/cuda/lib64/
7sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*

This avoids trying to automate an authenticated website download directly from the VM.

Verify the installation

After installation, make sure the files are where your CUDA installation expects them.

ls /usr/local/cuda/include/cudnn*.h
ls /usr/local/cuda/lib64/libcudnn*

You can also check the dynamic linker view:

ldconfig -p | grep cudnn

If your framework still cannot find cuDNN, the problem is often library-path configuration or a version mismatch with CUDA.

Alternative: use NVIDIA package repositories

Depending on your distribution and CUDA version, NVIDIA may provide repository-based installation instructions. That can be cleaner than manual archive copying because the package manager tracks installed files and upgrades.

The exact package names vary by distro and CUDA version, so follow the cuDNN release instructions that match your environment rather than copying a random command from a different setup.

This is usually the better choice when:

• You manage multiple machines
• You want repeatable installs
• You prefer package-manager ownership over manual file copies

What about downloading directly from the VM with wget

People often want a one-line wget from the instance straight to the NVIDIA website. The reason this is unreliable is that the cuDNN download frequently requires authentication and license acceptance tied to a browser session.

It is technically possible in some situations to reuse browser cookies or authenticated URLs, but that is brittle and not a great foundation for repeatable infrastructure.

If you need a reproducible setup, choose one of these instead:

• Use a repository-based install
• Copy a previously downloaded archive to the VM
• Use a prebuilt deep learning image that already includes the GPU stack

Common Pitfalls

The biggest mistake is assuming any cuDNN archive will work with any CUDA version. CUDA and cuDNN compatibility matters, so verify the pairing before installing.

Another issue is copying the files into the wrong CUDA location. If CUDA is not installed under /usr/local/cuda, adjust the paths accordingly.

Developers also forget that a successful file copy is not the same as a usable runtime setup. The framework still needs to locate compatible cuDNN libraries at runtime.

Finally, do not spend too much time trying to force a brittle anonymous download flow from the NVIDIA website when a package manager, uploaded archive, or prebuilt image is more reliable.

Summary

• A direct unauthenticated wget from the NVIDIA website is usually not the best cuDNN installation path.
• The most reliable workflow is manual download plus gcloud compute scp, then install on the VM.
• Repository-based installation is often better for repeatable setups.
• Check CUDA and cuDNN version compatibility before installing.
• Verify both file placement and runtime library visibility after installation.