CUDA Quick Start Guide

Minimal first-steps instructions to get CUDA running on a standard system.

1. Introduction

This guide covers the basic instructions needed to install CUDA and verify that a CUDA application can run on each supported platform.

These instructions are intended to be used on a clean installation of a supported platform. For questions which are not answered in this document, please refer to the Windows Installation Guide and Linux Installation Guide.

The CUDA installation packages can be found on the CUDA Downloads Page.

2. Windows

When installing CUDA on Windows, you can choose between the Network Installer and the Local Installer. The Network Installer allows you to download only the files you need. The Local Installer is a stand-alone installer with a large initial download. For more details, refer to the Windows Installation Guide.

2.1. Network Installer

Perform the following steps to install CUDA and verify the installation.

Launch the downloaded installer package.
Read and accept the EULA.
Select next to download and install all components.
Once the download completes, the installation will begin automatically.
Once the installation completes, click “next” to acknowledge the Nsight Visual Studio Edition installation summary.
Click close to close the installer.
Navigate to the Samples’ nbody directory in https://github.com/NVIDIA/cuda-samples/tree/master/Samples/5_Domain_Specific/nbody.
Open the nbody Visual Studio solution file for the version of Visual Studio you have installed, for example, nbody_vs2019.sln.
Open the Build menu within Visual Studio and click Build Solution.
Navigate to the CUDA Samples build directory and run the nbody sample.

Note

Run samples by navigating to the executable’s location, otherwise it will fail to locate dependent resources.

2.2. Local Installer

Perform the following steps to install CUDA and verify the installation.

Launch the downloaded installer package.
Read and accept the EULA.
Select next to install all components.
Once the installation completes, click next to acknowledge the Nsight Visual Studio Edition installation summary.
Click close to close the installer.
Navigate to the Samples’ nbody directory in https://github.com/NVIDIA/cuda-samples/tree/master/Samples/5_Domain_Specific/nbody.
Open the nbody Visual Studio solution file for the version of Visual Studio you have installed.
Open the Build menu within Visual Studio and click Build Solution.
Navigate to the CUDA Samples build directory and run the nbody sample.

Note

Run samples by navigating to the executable’s location, otherwise it will fail to locate dependent resources.

2.3. Pip Wheels - Windows

NVIDIA provides Python Wheels for installing CUDA through pip, primarily for using CUDA with Python. These packages are intended for runtime use and do not currently include developer tools (these can be installed separately).

Please note that with this installation method, CUDA installation environment is managed via pip and additional care must be taken to set up your host environment to use CUDA outside the pip environment.

Prerequisites

To install Wheels, you must first install the nvidia-pyindex package, which is required in order to set up your pip installation to fetch additional Python modules from the NVIDIA NGC PyPI repo. If your pip and setuptools Python modules are not up-to-date, then use the following command to upgrade these Python modules. If these Python modules are out-of-date then the commands which follow later in this section may fail.

py -m pip install --upgrade setuptools pip wheel

You should now be able to install the nvidia-pyindex module.

py -m pip install nvidia-pyindex

If your project is using a requirements.txt file, then you can add the following line to your requirements.txt file as an alternative to installing the nvidia-pyindex package:

--extra-index-url https://pypi.ngc.nvidia.com

Procedure

Install the CUDA runtime package:

py -m pip install nvidia-cuda-runtime-cu12

Optionally, install additional packages as listed below using the following command:

py -m pip install nvidia-<library>

Metapackages

The following metapackages will install the latest version of the named component on Windows for the indicated CUDA version. “cu12” should be read as “cuda12”.

nvidia-cuda-runtime-cu12
nvidia-cuda-cupti-cu12
nvidia-cuda-nvcc-cu12
nvidia-nvml-dev-cu12
nvidia-cuda-nvrtc-cu12
nvidia-nvtx-cu12
nvidia-cuda-sanitizer-api-cu12
nvidia-cublas-cu12
nvidia-cufft-cu12
nvidia-curand-cu12
nvidia-cusolver-cu12
nvidia-cusparse-cu12
nvidia-npp-cu12
nvidia-nvjpeg-cu12

These metapackages install the following packages:

nvidia-nvml-dev-cu126
nvidia-cuda-nvcc-cu126
nvidia-cuda-runtime-cu126
nvidia-cuda-cupti-cu126
nvidia-cublas-cu126
nvidia-cuda-sanitizer-api-cu126
nvidia-nvtx-cu126
nvidia-cuda-nvrtc-cu126
nvidia-npp-cu126
nvidia-cusparse-cu126
nvidia-cusolver-cu126
nvidia-curand-cu126
nvidia-cufft-cu126
nvidia-nvjpeg-cu126

2.4. Conda

The Conda packages are available at https://anaconda.org/nvidia.

Installation

To perform a basic install of all CUDA Toolkit components using Conda, run the following command:

conda install cuda -c nvidia

Uninstallation

To uninstall the CUDA Toolkit using Conda, run the following command:

conda remove cuda

3. Linux

CUDA on Linux can be installed using an RPM, Debian, Runfile, or Conda package, depending on the platform being installed on.

3.1. Linux x86_64

For development on the x86_64 architecture. In some cases, x86_64 systems may act as host platforms targeting other architectures. See the Linux Installation Guide for more details.

3.1.1. Redhat / CentOS

When installing CUDA on Redhat or CentOS, you can choose between the Runfile Installer and the RPM Installer. The Runfile Installer is only available as a Local Installer. The RPM Installer is available as both a Local Installer and a Network Installer. The Network Installer allows you to download only the files you need. The Local Installer is a stand-alone installer with a large initial download. In the case of the RPM installers, the instructions for the Local and Network variants are the same. For more details, refer to the Linux Installation Guide.

3.1.1.1. RPM Installer

Perform the following steps to install CUDA and verify the installation.

Install EPEL to satisfy the DKMS dependency by following the instructions at EPEL’s website.

Enable optional repos:

On RHEL 8 Linux only, execute the following steps to enable optional repositories.

On x86_64 workstation:

subscription-manager repos --enable=rhel-8-for-x86_64-appstream-rpms
subscription-manager repos --enable=rhel-8-for-x86_64-baseos-rpms
subscription-manager repos --enable=codeready-builder-for-rhel-8-x86_64-rpms

Install the repository meta-data, clean the yum cache, and install CUDA:

sudo rpm --install cuda-repo-<distro>-<version>.<architecture>.rpm
sudo rpm --erase gpg-pubkey-7fa2af80*
sudo yum clean expire-cache
sudo yum install cuda

Reboot the system to load the NVIDIA drivers:
```
sudo reboot
```

Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:

export PATH=/usr/local/cuda-12.6/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-12.6/lib64\
                         ${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

Install a writable copy of the samples from https://github.com/nvidia/cuda-samples, then build and run the nbody sample using the Linux instructions in https://github.com/NVIDIA/cuda-samples/tree/master/Samples/5_Domain_Specific/nbody.

Note

Run samples by navigating to the executable’s location, otherwise it will fail to locate dependent resources.

3.1.1.2. Runfile Installer

Perform the following steps to install CUDA and verify the installation.

Disable the Nouveau drivers:
1. Create a file at /etc/modprobe.d/blacklist-nouveau.conf with the following contents:
```
blacklist nouveau
options nouveau modeset=0
```
2. Regenerate the kernel initramfs:
```
sudo dracut --force
```
Reboot into runlevel 3 by temporarily adding the number “3” and the word “nomodeset” to the end of the system’s kernel boot parameters.
Run the installer silently to install with the default selections (implies acceptance of the EULA):
```
sudo sh cuda_<version>_linux.run --silent
```
Create an xorg.conf file to use the NVIDIA GPU for display:
```
sudo nvidia-xconfig
```
Reboot the system to load the graphical interface:
```
sudo reboot
```

Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:

export PATH=/usr/local/cuda-12.6/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-12.6/lib64\
                         ${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

Install a writable copy of the samples from https://github.com/nvidia/cuda-samples, then build and run the nbody sample using the Linux instructions in https://github.com/NVIDIA/cuda-samples/tree/master/Samples/5_Domain_Specific/nbody.

Note

Run samples by navigating to the executable’s location, otherwise it will fail to locate dependent resources.

3.1.2. Fedora

When installing CUDA on Fedora, you can choose between the Runfile Installer and the RPM Installer. The Runfile Installer is only available as a Local Installer. The RPM Installer is available as both a Local Installer and a Network Installer. The Network Installer allows you to download only the files you need. The Local Installer is a stand-alone installer with a large initial download. In the case of the RPM installers, the instructions for the Local and Network variants are the same. For more details, refer to the Linux Installation Guide.

3.1.2.1. RPM Installer

Perform the following steps to install CUDA and verify the installation.

Install the RPMFusion free repository to satisfy the Akmods dependency:

su -c 'dnf install --nogpgcheck http://download1.rpmfusion.org/free/fedora/rpmfusion-free-release-$(rpm -E %fedora).noarch.rpm'

Install the repository meta-data, clean the dnf cache, and install CUDA:

sudo rpm --install cuda-repo-<distro>-<version>.<architecture>.rpm
sudo rpm --erase gpg-pubkey-7fa2af80*
sudo dnf clean expire-cache
sudo dnf install cuda

Reboot the system to load the NVIDIA drivers:
```
sudo reboot
```

Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:

export PATH=/usr/local/cuda-12.6/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-12.6/lib64\
                         ${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

Install a writable copy of the samples from https://github.com/nvidia/cuda-samples, then build and run the nbody sample using the Linux instructions in https://github.com/NVIDIA/cuda-samples/tree/master/Samples/5_Domain_Specific/nbody.

Note

Run samples by navigating to the executable’s location, otherwise it will fail to locate dependent resources.

3.1.2.2. Runfile Installer

Perform the following steps to install CUDA and verify the installation.

Disable the Nouveau drivers:
1. Create a file at /usr/lib/modprobe.d/blacklist-nouveau.conf with the following contents:
```
blacklist nouveau
options nouveau modeset=0
```
2. Regenerate the kernel initramfs:
```
sudo dracut --force
```
3. Run the below command:
```
sudo grub2-mkconfig -o /boot/grub2/grub.cfg
```
4. Reboot the system:
```
sudo reboot
```
Reboot into runlevel 3 by temporarily adding the number “3” and the word “nomodeset” to the end of the system’s kernel boot parameters.
Run the installer silently to install with the default selections (implies acceptance of the EULA):
```
sudo sh cuda_<version>_linux.run --silent
```
Create an xorg.conf file to use the NVIDIA GPU for display:
```
sudo nvidia-xconfig
```
Reboot the system to load the graphical interface.

Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:

export PATH=/usr/local/cuda-12.6/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-12.6/lib64\
                         ${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

Install a writable copy of the samples from https://github.com/nvidia/cuda-samples, then build and run the nbody sample using the Linux instructions in https://github.com/NVIDIA/cuda-samples/tree/master/Samples/5_Domain_Specific/nbody.

Note

Run samples by navigating to the executable’s location, otherwise it will fail to locate dependent resources.

3.1.3. SUSE Linux Enterprise Server

When installing CUDA on SUSE Linux Enterprise Server, you can choose between the Runfile Installer and the RPM Installer. The Runfile Installer is only available as a Local Installer. The RPM Installer is available as both a Local Installer and a Network Installer. The Network Installer allows you to download only the files you need. The Local Installer is a stand-alone installer with a large initial download. In the case of the RPM installers, the instructions for the Local and Network variants are the same. For more details, refer to the Linux Installation Guide.

3.1.3.1. RPM Installer

Perform the following steps to install CUDA and verify the installation.

Install the repository meta-data, refresh the Zypper cache, update the GPG key, and install CUDA:

sudo rpm --install cuda-repo-<distro>-<version>.<architecture>.rpm
sudo SUSEConnect --product PackageHub/15/x86_64
sudo zypper refresh
sudo rpm --erase gpg-pubkey-7fa2af80*
sudo dnf config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/$distro/$arch/cuda-$distro.repo
sudo zypper install cuda

Add the user to the video group:
```
sudo usermod -a -G video <username>
```
Reboot the system to load the NVIDIA drivers:
```
sudo reboot
```

Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:

export PATH=/usr/local/cuda-12.6/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-12.6/lib64\
                         ${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

Install a writable copy of the samples from https://github.com/nvidia/cuda-samples, then build and run the vectorAdd sample using the Linux instructions in https://github.com/NVIDIA/cuda-samples/tree/master/Samples/0_Introduction/vectorAdd.

Note

Run samples by navigating to the executable’s location, otherwise it will fail to locate dependent resources.

3.1.3.2. Runfile Installer

Perform the following steps to install CUDA and verify the installation.

Reboot into runlevel 3 by temporarily adding the number “3” and the word “nomodeset” to the end of the system’s kernel boot parameters.
Run the installer silently to install with the default selections (implies acceptance of the EULA):
```
sudo sh cuda_<version>_linux.run --silent
```
Create an xorg.conf file to use the NVIDIA GPU for display:
```
sudo nvidia-xconfig
```
Reboot the system to load the graphical interface:
```
sudo reboot
```

Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:

export PATH=/usr/local/cuda-12.6/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-12.6/lib64\
                         ${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

Install a writable copy of the samples from https://github.com/nvidia/cuda-samples, then build and run the vectorAdd sample using the Linux instructions in https://github.com/NVIDIA/cuda-samples/tree/master/Samples/0_Introduction/vectorAdd.

Note

Run samples by navigating to the executable’s location, otherwise it will fail to locate dependent resources.

3.1.4. OpenSUSE

When installing CUDA on OpenSUSE, you can choose between the Runfile Installer and the RPM Installer. The Runfile Installer is only available as a Local Installer. The RPM Installer is available as both a Local Installer and a Network Installer. The Network Installer allows you to download only the files you need. The Local Installer is a stand-alone installer with a large initial download. In the case of the RPM installers, the instructions for the Local and Network variants are the same. For more details, refer to the Linux Installation Guide.

3.1.4.1. RPM Installer

Perform the following steps to install CUDA and verify the installation.

Install the repository meta-data, refresh the Zypper cache, and install CUDA:

sudo rpm --install cuda-repo-<distro>-<version>.<architecture>.rpm
sudo rpm --erase gpg-pubkey-7fa2af80*
sudo zypper refresh
sudo zypper install cuda

Add the user to the video group:
```
sudo usermod -a -G video <username>
```
Reboot the system to load the NVIDIA drivers:
```
sudo reboot
```

Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:

export PATH=/usr/local/cuda-12.6/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-12.6/lib64\
                         ${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

Install a writable copy of the samples from https://github.com/nvidia/cuda-samples, then build and run the nbody sample using the Linux instructions in https://github.com/NVIDIA/cuda-samples/tree/master/Samples/5_Domain_Specific/nbody.

Note

Run samples by navigating to the executable’s location, otherwise it will fail to locate dependent resources.

3.1.4.2. Runfile Installer

Perform the following steps to install CUDA and verify the installation.

Disable the Nouveau drivers:
1. Create a file at /etc/modprobe.d/blacklist-nouveau.conf with the following contents:
```
blacklist nouveau
options nouveau modeset=0
```
2. Regenerate the kernel initrd:
```
sudo /sbin/mkinitrd
```
Reboot into runlevel 3 by temporarily adding the number “3” and the word “nomodeset” to the end of the system’s kernel boot parameters.
Run the installer silently to install with the default selections (implies acceptance of the EULA):
```
sudo sh cuda_<version>_linux.run --silent
```
Create an xorg.conf file to use the NVIDIA GPU for display:
```
sudo nvidia-xconfig
```
Reboot the system to load the graphical interface:
```
sudo reboot
```

Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:

export PATH=/usr/local/cuda-12.6/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-12.6/lib64\
                    ${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

Install a writable copy of the samples from https://github.com/nvidia/cuda-samples, then build and run the nbody sample using the Linux instructions in https://github.com/NVIDIA/cuda-samples/tree/master/Samples/5_Domain_Specific/nbody.

Note

Run samples by navigating to the executable’s location, otherwise it will fail to locate dependent resources.

3.1.5. Amazon Linux 2023

3.1.5.1. Prepare Amazon Linux 2023

Perform the pre-installation actions.

The kernel headers and development packages for the currently running kernel can be installed with:

sudo dnf install kernel-devel-$(uname -r) kernel-headers-$(uname -r) kernel-modules-extra-$(uname -r)

Choose an installation method: local repo or network repo.

3.1.5.2. Local Repo Installation for Amazon Linux

Install local repository on file system:

sudo rpm --install cuda-repo-amzn2023-X-Y-local-<version>*.x86_64.rpm

3.1.5.3. Network Repo Installation for Amazon Linux

Enable the network repository and clean the DN cache:

sudo dnf config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/amzn2023/x86_64/cuda-amzn2023.repo
sudo dnf clean expire-cache

3.1.5.4. Common Installation Instructions for Amazon Linux

These instructions apply to both local and network installation for Amazon Linux.

Install CUDA SDK:

sudo dnf module install nvidia-driver:latest-dkms
sudo dnf install cuda-toolkit

Install GPUDirect Filesystem:
```
sudo dnf install nvidia-gds
```
Add libcuda.so symbolic link, if necessary:

The libcuda.so library is installed in the /usr/lib{,64}/nvidia directory. For pre-existing projects which use libcuda.so, it may be useful to add a symbolic link from libcuda.so in the /usr/lib{,64} directory.
Reboot the system:
```
sudo reboot
```
Perform the post-installation actions.

3.1.6. Pip Wheels - Linux

NVIDIA provides Python Wheels for installing CUDA through pip, primarily for using CUDA with Python. These packages are intended for runtime use and do not currently include developer tools (these can be installed separately).

Please note that with this installation method, CUDA installation environment is managed via pip and additional care must be taken to set up your host environment to use CUDA outside the pip environment.

Prerequisites

To install Wheels, you must first install the nvidia-pyindex package, which is required in order to set up your pip installation to fetch additional Python modules from the NVIDIA NGC PyPI repo. If your pip and setuptools Python modules are not up-to-date, then use the following command to upgrade these Python modules. If these Python modules are out-of-date then the commands which follow later in this section may fail.

python3 -m pip install --upgrade setuptools pip wheel

You should now be able to install the nvidia-pyindex module.

python3 -m pip install nvidia-pyindex

If your project is using a requirements.txt file, then you can add the following line to your requirements.txt file as an alternative to installing the nvidia-pyindex package:

--extra-index-url https://pypi.ngc.nvidia.com

Procedure

Install the CUDA runtime package:

python3 -m pip install nvidia-cuda-runtime-cu12

Optionally, install additional packages as listed below using the following command:

python3 -m pip install nvidia-<library>

Metapackages

The following metapackages will install the latest version of the named component on Linux for the indicated CUDA version. “cu12” should be read as “cuda12”.

nvidia-cuda-runtime-cu12
nvidia-cuda-cupti-cu12
nvidia-cuda-nvcc-cu12
nvidia-nvml-dev-cu12
nvidia-cuda-nvrtc-cu12
nvidia-nvtx-cu12
nvidia-cuda-sanitizer-api-cu12
nvidia-cublas-cu12
nvidia-cufft-cu12
nvidia-curand-cu12
nvidia-cusolver-cu12
nvidia-cusparse-cu12
nvidia-npp-cu12
nvidia-nvjpeg-cu12
nvidia-opencl-cu12
nvidia-nvjitlink-cu12

These metapackages install the following packages:

nvidia-nvml-dev-cu126
nvidia-cuda-nvcc-cu126
nvidia-cuda-runtime-cu126
nvidia-cuda-cupti-cu126
nvidia-cublas-cu126
nvidia-cuda-sanitizer-api-cu126
nvidia-nvtx-cu126
nvidia-cuda-nvrtc-cu126
nvidia-npp-cu126
nvidia-cusparse-cu126
nvidia-cusolver-cu126
nvidia-curand-cu126
nvidia-cufft-cu126
nvidia-nvjpeg-cu126
nvidia-opencl-cu126
nvidia-nvjitlink-cu126

3.1.7. Conda

The Conda packages are available at https://anaconda.org/nvidia.

Installation

To perform a basic install of all CUDA Toolkit components using Conda, run the following command:

conda install cuda -c nvidia

Uninstallation

To uninstall the CUDA Toolkit using Conda, run the following command:

conda remove cuda

3.1.8. WSL

These instructions must be used if you are installing in a WSL environment. Do not use the Ubuntu instructions in this case.

Install repository meta-data

sudo dpkg -i cuda-repo-<distro>_<version>_<architecture>.deb

Update the CUDA public GPG key

sudo apt-key del 7fa2af80

When installing using the local repo:

sudo cp /var/cuda-repo-ubuntu2004-12-0-local/cuda-*-keyring.gpg /usr/share/keyrings/

When installing using the network repo:

wget https://developer.download.nvidia.com/compute/cuda/repos/<distro>/<arch>/cuda-keyring_1.1-1_all.deb
sudo dpkg -i cuda-keyring_1.1-1_all.deb

Pin file to prioritize CUDA repository:

wget https://developer.download.nvidia.com/compute/cuda/repos/<distro>/<architecture>/cuda-<distro>.pin
sudo mv cuda-<distro>.pin /etc/apt/preferences.d/cuda-repository-pin-600

Update the Apt repository cache and install CUDA
```
sudo apt-get update
sudo apt-get install cuda
```

3.1.9. Ubuntu

When installing CUDA on Ubuntu, you can choose between the Runfile Installer and the Debian Installer. The Runfile Installer is only available as a Local Installer. The Debian Installer is available as both a Local Installer and a Network Installer. The Network Installer allows you to download only the files you need. The Local Installer is a stand-alone installer with a large initial download. In the case of the Debian installers, the instructions for the Local and Network variants are the same. For more details, refer to the Linux Installation Guide.

3.1.9.1. Debian Installer

Perform the following steps to install CUDA and verify the installation.

Install the repository meta-data, update the GPG key, update the apt-get cache, and install CUDA:

sudo dpkg --install cuda-repo-<distro>-<version>.<architecture>.deb
sudo apt-key del 7fa2af80
wget https://developer.download.nvidia.com/compute/cuda/repos/<distro>/<arch>/cuda-keyring_1.1-1_all.deb
sudo dpkg -i cuda-keyring_1.1-1_all.deb
sudo add-apt-repository contrib
sudo apt-get update
sudo apt-get -y install cuda

Reboot the system to load the NVIDIA drivers:
```
sudo reboot
```

Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:

export PATH=/usr/local/cuda-12.6/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-12.6/lib64\
                         ${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

Install a writable copy of the samples from https://github.com/nvidia/cuda-samples, then build and run the nbody sample using the Linux instructions in https://github.com/NVIDIA/cuda-samples/tree/master/Samples/5_Domain_Specific/nbody.

Note

Run samples by navigating to the executable’s location, otherwise it will fail to locate dependent resources.

3.1.9.2. Runfile Installer

Perform the following steps to install CUDA and verify the installation.

Disable the Nouveau drivers:
1. Create a file at /etc/modprobe.d/blacklist-nouveau.conf with the following contents:
```
blacklist nouveau
options nouveau modeset=0
```
2. Regenerate the kernel initramfs:
```
sudo update-initramfs -u
```
Reboot into runlevel 3 by temporarily adding the number “3” and the word “nomodeset” to the end of the system’s kernel boot parameters.
Run the installer silently to install with the default selections (implies acceptance of the EULA):
```
sudo sh cuda_<version>_linux.run --silent
```
Create an xorg.conf file to use the NVIDIA GPU for display:
```
sudo nvidia-xconfig
```
Reboot the system to load the graphical interface:
```
sudo reboot
```

Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:

export PATH=/usr/local/cuda-12.6/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-12.6/lib64\
                         ${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

Install a writable copy of the samples from https://github.com/nvidia/cuda-samples, then build and run the nbody sample using the Linux instructions in https://github.com/NVIDIA/cuda-samples/tree/master/Samples/5_Domain_Specific/nbody.

Note

Run samples by navigating to the executable’s location, otherwise it will fail to locate dependent resources.

3.1.10. Debian

When installing CUDA on Debian 10, you can choose between the Runfile Installer and the Debian Installer. The Runfile Installer is only available as a Local Installer. The Debian Installer is available as both a Local Installer and a Network Installer. The Network Installer allows you to download only the files you need. The Local Installer is a stand-alone installer with a large initial download. For more details, refer to the Linux Installation Guide.

3.1.10.1. Debian Installer

Perform the following steps to install CUDA and verify the installation.

Install the repository meta-data, remove old GPG key, install GPG key, update the apt-get cache, and install CUDA:

sudo dpkg -i cuda-repo-<distro>_<version>_<architecture>.deb
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/debian10/x86_64/7fa2af80.pub
sudo apt-key del 7fa2af80
wget https://developer.download.nvidia.com/compute/cuda/repos/<distro>/<arch>/cuda-keyring_1.1-1_all.deb
sudo dpkg -i cuda-keyring_1.1-1_all.deb
sudo add-apt-repository contrib
sudo apt-get update
sudo apt-get -y install cuda

Reboot the system to load the NVIDIA drivers:
```
sudo reboot
```

Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:

export PATH=/usr/local/cuda-12.6/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-12.6/lib64\
                         ${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

Install a writable copy of the samples from https://github.com/nvidia/cuda-samples, then build and run the nbody sample using the Linux instructions in https://github.com/NVIDIA/cuda-samples/tree/master/Samples/5_Domain_Specific/nbody.

Note

Run samples by navigating to the executable’s location, otherwise it will fail to locate dependent resources.

3.1.10.2. Runfile Installer

Perform the following steps to install CUDA and verify the installation.

Disable the Nouveau drivers:
1. Create a file at /etc/modprobe.d/blacklist-nouveau.conf with the following contents:
```
blacklist nouveau
options nouveau modeset=0
```
2. Regenerate the kernel initramfs:
```
sudo update-initramfs -u
```
Reboot into runlevel 3 by temporarily adding the number “3” and the word “nomodeset” to the end of the system’s kernel boot parameters.
Run the installer silently to install with the default selections (implies acceptance of the EULA):
```
sudo sh cuda_<version>_linux.run --silent
```
Create an xorg.conf file to use the NVIDIA GPU for display:
```
sudo nvidia-xconfig
```
Reboot the system to load the graphical interface:
```
sudo reboot
```

Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:

export PATH=/usr/local/cuda-12.6/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-12.6/lib64\
                         ${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

Install a writable copy of the samples from https://github.com/nvidia/cuda-samples, then build and run the nbody sample using the Linux instructions in https://github.com/NVIDIA/cuda-samples/tree/master/Samples/5_Domain_Specific/nbody.

Note

Run samples by navigating to the executable’s location, otherwise it will fail to locate dependent resources.

4. Notices

4.1. Notice

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4.2. OpenCL

OpenCL is a trademark of Apple Inc. used under license to the Khronos Group Inc.

4.3. Trademarks

NVIDIA and the NVIDIA logo are trademarks or registered trademarks of NVIDIA Corporation in the U.S. and other countries. Other company and product names may be trademarks of the respective companies with which they are associated.