Nvidia Drivers (v595) and CUDA 12.1 Setup for Ubuntu 22.04 x86
Flush old installation
Update Packages
Install Kernel Headers and Build Tools
Install Nvidia Cuda Keyring
Pin Driver Branch (595 in this case, update as necessary)
Install the Driver
Reboot System
Verify Installation
You now have NVIDIA Drivers setup, Let's proceed to CUDA and cuDNN installation
Flush Old CUDA Installation
Install CUDA with NVIDIA Network Repo
Install cuDNN
Set Environment variables
Reboot
Verfiy installations
Verify visibility to python frameworks
Conclusion Let's face it, Windows is getting really stressful to work with as a developer in 2026. If you've tried to work with your GPU on TensorFlow for deep learning projects, you probably have discovered that you're either stuck with CPU-based compute, dated CUDA versions for older TensorFlow versions, or the hell that is setting up WSL2. Why not just switch to a Linux native environment like Ubuntu? Yeah, that's easily the safe and sane choice in 2026. Moreover, most of your production workloads will be on Linux servers, so might as well make the jump on your local setup too. I've struggled in time past to configure all the moving parts. This is because the information is scattered across and and people really do be saying random things on the internet. It can easily take hours to figure it out. And before you run off to ChatGeePeeDee to show you all the steps, I need you to understand that LLMs hallucinate and Linux will allow you run ANY command, including "removing the French language pack". So be careful what you run on your system from LLMs. This is an attempt to make it easy, especially as more people dump Windows for Linux for the sake of sanity away from the MicroSlop ecosystem. This setup was done for an Nvidia 3060 Laptop edition GPU (Use this as context to safety-check the steps as you go along). Let's just jump right in! TL;DR: CUDA 12.1 with cuDNN 8.9.x is the most stable version for TensorFlow and PyTorch simultaneously, so it's generally best to go with this version combination for now. Download cuDNN 8.9 for CUDA 12.1 from NVIDIA Developer: [https://developer.nvidia.com/rdp/cudnn-archive]
Choose Linux x86_64 / tar package for CUDA 12.1 Extract and copy files N.B: nvidia-smi will show the highest compatible CUDA version based on the installed NVIDIA driver version, whilst nvcc --version will show the currently installed CUDA version, so if they differ, this is totally fine. If you made it this far and followed all the steps, you should now be able to run your TensorFlow and PyTorch (and indeed other GPU-based workloads) on your NVIDIA GPU in your Ubuntu environment. Follow for more tech content around machine learning and data science. Templates let you quickly answer FAQs or store snippets for re-use. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse
$ -weight: 600;">sudo nvidia-uninstall
-weight: 600;">sudo -weight: 500;">apt purge -y '^nvidia-*' '^libnvidia-*'
-weight: 600;">sudo rm -r /var/lib/dkms/nvidia
-weight: 600;">sudo -weight: 500;">apt -y autoremove
-weight: 600;">sudo -weight: 500;">update-initramfs -c -k `uname -r`
-weight: 600;">sudo -weight: 500;">update-grub2
read -p "Press any key to reboot... " -n1 -s
-weight: 600;">sudo reboot
-weight: 600;">sudo nvidia-uninstall
-weight: 600;">sudo -weight: 500;">apt purge -y '^nvidia-*' '^libnvidia-*'
-weight: 600;">sudo rm -r /var/lib/dkms/nvidia
-weight: 600;">sudo -weight: 500;">apt -y autoremove
-weight: 600;">sudo -weight: 500;">update-initramfs -c -k `uname -r`
-weight: 600;">sudo -weight: 500;">update-grub2
read -p "Press any key to reboot... " -n1 -s
-weight: 600;">sudo reboot
-weight: 600;">sudo nvidia-uninstall
-weight: 600;">sudo -weight: 500;">apt purge -y '^nvidia-*' '^libnvidia-*'
-weight: 600;">sudo rm -r /var/lib/dkms/nvidia
-weight: 600;">sudo -weight: 500;">apt -y autoremove
-weight: 600;">sudo -weight: 500;">update-initramfs -c -k `uname -r`
-weight: 600;">sudo -weight: 500;">update-grub2
read -p "Press any key to reboot... " -n1 -s
-weight: 600;">sudo reboot
-weight: 600;">sudo -weight: 500;">apt -weight: 500;">update
-weight: 600;">sudo -weight: 500;">apt -weight: 500;">update
-weight: 600;">sudo -weight: 500;">apt -weight: 500;">update
-weight: 600;">sudo -weight: 500;">apt -weight: 500;">install -y linux-headers-$(uname -r) build-essential dkms
-weight: 600;">sudo -weight: 500;">apt -weight: 500;">install -y linux-headers-$(uname -r) build-essential dkms
-weight: 600;">sudo -weight: 500;">apt -weight: 500;">install -y linux-headers-$(uname -r) build-essential dkms
-weight: 500;">wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
-weight: 600;">sudo dpkg -i cuda-keyring_1.1-1_all.deb
-weight: 600;">sudo -weight: 500;">apt -weight: 500;">update
-weight: 500;">wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
-weight: 600;">sudo dpkg -i cuda-keyring_1.1-1_all.deb
-weight: 600;">sudo -weight: 500;">apt -weight: 500;">update
-weight: 500;">wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
-weight: 600;">sudo dpkg -i cuda-keyring_1.1-1_all.deb
-weight: 600;">sudo -weight: 500;">apt -weight: 500;">update
-weight: 600;">sudo -weight: 500;">apt -weight: 500;">install -y nvidia-driver-pinning-595
-weight: 600;">sudo -weight: 500;">apt -weight: 500;">install -y nvidia-driver-pinning-595
-weight: 600;">sudo -weight: 500;">apt -weight: 500;">install -y nvidia-driver-pinning-595
-weight: 600;">sudo -weight: 500;">apt -weight: 500;">install -y nvidia-open
-weight: 600;">sudo -weight: 500;">apt -weight: 500;">install -y nvidia-open
-weight: 600;">sudo -weight: 500;">apt -weight: 500;">install -y nvidia-open
-weight: 600;">sudo reboot
-weight: 600;">sudo reboot
-weight: 600;">sudo reboot
-weight: 600;">sudo -weight: 500;">apt -weight: 500;">remove --purge -y 'cuda*' 'libcudnn*'
-weight: 600;">sudo rm -rf /usr/local/cuda*
-weight: 600;">sudo -weight: 500;">apt -weight: 500;">remove --purge -y 'cuda*' 'libcudnn*'
-weight: 600;">sudo rm -rf /usr/local/cuda*
-weight: 600;">sudo -weight: 500;">apt -weight: 500;">remove --purge -y 'cuda*' 'libcudnn*'
-weight: 600;">sudo rm -rf /usr/local/cuda*
# Download CUDA 12.1 package keyring
-weight: 500;">wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
-weight: 600;">sudo dpkg -i cuda-keyring_1.1-1_all.deb
-weight: 600;">sudo -weight: 500;">apt -weight: 500;">update # Install CUDA 12.1 toolkit
-weight: 600;">sudo -weight: 500;">apt -weight: 500;">install -y cuda-toolkit-12-1
# Download CUDA 12.1 package keyring
-weight: 500;">wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
-weight: 600;">sudo dpkg -i cuda-keyring_1.1-1_all.deb
-weight: 600;">sudo -weight: 500;">apt -weight: 500;">update # Install CUDA 12.1 toolkit
-weight: 600;">sudo -weight: 500;">apt -weight: 500;">install -y cuda-toolkit-12-1
# Download CUDA 12.1 package keyring
-weight: 500;">wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
-weight: 600;">sudo dpkg -i cuda-keyring_1.1-1_all.deb
-weight: 600;">sudo -weight: 500;">apt -weight: 500;">update # Install CUDA 12.1 toolkit
-weight: 600;">sudo -weight: 500;">apt -weight: 500;">install -y cuda-toolkit-12-1
tar -xvf download-path/cudnn-file-name.tar.xz
cd extracted-folder-path
-weight: 600;">sudo cp -P include/cudnn*.h /usr/local/cuda/include/
-weight: 600;">sudo cp -P lib/libcudnn* /usr/local/cuda/lib64/
-weight: 600;">sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*
tar -xvf download-path/cudnn-file-name.tar.xz
cd extracted-folder-path
-weight: 600;">sudo cp -P include/cudnn*.h /usr/local/cuda/include/
-weight: 600;">sudo cp -P lib/libcudnn* /usr/local/cuda/lib64/
-weight: 600;">sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*
tar -xvf download-path/cudnn-file-name.tar.xz
cd extracted-folder-path
-weight: 600;">sudo cp -P include/cudnn*.h /usr/local/cuda/include/
-weight: 600;">sudo cp -P lib/libcudnn* /usr/local/cuda/lib64/
-weight: 600;">sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*
nano ~/.bashrc
nano ~/.bashrc
nano ~/.bashrc
export CUDA_HOME=/usr/local/cuda
export PATH=$CUDA_HOME/bin:$PATH
export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH
export CUDA_HOME=/usr/local/cuda
export PATH=$CUDA_HOME/bin:$PATH
export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH
export CUDA_HOME=/usr/local/cuda
export PATH=$CUDA_HOME/bin:$PATH
export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH
source ~/.bashrc
source ~/.bashrc
source ~/.bashrc
-weight: 600;">sudo reboot
-weight: 600;">sudo reboot
-weight: 600;">sudo reboot
nvcc --version
echo $CUDA_HOME
echo $PATH
echo $LD_LIBRARY_PATH
nvcc --version
echo $CUDA_HOME
echo $PATH
echo $LD_LIBRARY_PATH
nvcc --version
echo $CUDA_HOME
echo $PATH
echo $LD_LIBRARY_PATH
import tensorflow as tf
print(tf.config.list_physical_devices('GPU'))
import tensorflow as tf
print(tf.config.list_physical_devices('GPU'))
import tensorflow as tf
print(tf.config.list_physical_devices('GPU'))
Output: [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
Output: [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
Output: [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')] - Download cuDNN 8.9 for CUDA 12.1 from NVIDIA Developer: [https://developer.nvidia.com/rdp/cudnn-archive]
Choose Linux x86_64 / tar package for CUDA 12.1
- Extract and copy files - Replace or Paste these environment variables for CUDA: - Apply Changes - Create a python virtual environment
- Paste this in a .py file:
