NVIDIA CUDA Installation Guide for Linux

The installation instructions for the CUDA Toolkit on Linux.

1. Introduction

CUDA® is a parallel computing platform and programming model invented by NVIDIA®. It enables dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU).

CUDA was developed with several design goals in mind:

  • Provide a small set of extensions to standard programming languages, like C, that enable a straightforward implementation of parallel algorithms. With CUDA C/C++, programmers can focus on the task of parallelization of the algorithms rather than spending time on their implementation.

  • Support heterogeneous computation where applications use both the CPU and GPU. Serial portions of applications are run on the CPU, and parallel portions are offloaded to the GPU. As such, CUDA can be incrementally applied to existing applications. The CPU and GPU are treated as separate devices that have their own memory spaces. This configuration also allows simultaneous computation on the CPU and GPU without contention for memory resources.

CUDA-capable GPUs have hundreds of cores that can collectively run thousands of computing threads. These cores have shared resources including a register file and a shared memory. The on-chip shared memory allows parallel tasks running on these cores to share data without sending it over the system memory bus.

This guide will show you how to install and check the correct operation of the CUDA development tools.

1.1. System Requirements

To use NVIDIA CUDA on your system, you will need the following installed:

The CUDA development environment relies on tight integration with the host development environment, including the host compiler and C runtime libraries, and is therefore only supported on distribution versions that have been qualified for this CUDA Toolkit release.

The following table lists the supported Linux distributions. Please review the footnotes associated with the table.

Table 1 Native Linux Distribution Support in CUDA 12.3 Update 2

Distribution

Kernel1

Default GCC

GLIBC

x86_64

RHEL 9.y (y <= 2)

5.14.0-284

11.3.1

2.34

RHEL 8.y (y <= 8)

4.18.0-477

8.5.0

2.28

RHEL 7.y (y <= 9)

3.10.0-1160

6.x

2.17

CentOS 7.y (y <= 9)

3.10.0-1160

6.x

2.17

OpenSUSE Leap 15.y (y <= 5)

5.14.21-150500

7.5.0

2.31

Rocky Linux 8.y (y<=8)

4.18.0-477

8.5.0

2.28

Rocky Linux 9.y (y<=2)

5.14.0-284

11.3.1

2.34

SUSE SLES 15.y (y <= 5)

5.14.21-150500

7.5.0

2.31

Ubuntu 22.04.z (z <= 3) LTS

6.2.0-26

11.4.0

2.35

Ubuntu 20.04.z (z <= 6) LTS

5.15.0-67

9.4.0

2.31

Debian 12.x (x<=2)

6.1.0-13

12.2.0

2.36

Debian 11.y (y<=8)

5.10.197-1

10.2.1

2.31

Debian 10.z (z<=13)

4.19.0-21

8.3.0

2.28

Fedora 37

6.0.7-301

12.2.1

2.36

KylinOS V10 SP2

4.19.90-25.14.v2101.ky10

7.3.0

2.28

Arm64 sbsa

RHEL 9.y (y <= 2)

5.14.0-284

11.3.1

2.34

RHEL 8.y (y <= 8)

4.18.0-477

8.5.0

2.28

SUSE SLES 15.y (y <= 5)

5.14.21-150500

7.5.0

2.31

Ubuntu 22.04 LTS (z <= 3) LTS

5.15.0-79

11.4.0

2.35

Ubuntu 20.04.z (z <= 6) LTS

5.4.0-125

9.4.0

2.31

POWER 9

RHEL 8.y (y <= 8)

4.18.0-477

8.5.0

2.28

  1. The following notes apply to the kernel versions supported by CUDA:

  1. L4T provides a Linux kernel and a sample root filesystem derived from Ubuntu 20.04. For more details, visit https://developer.nvidia.com/embedded/jetson-linux.

1.2. OS Support Policy

  • CUDA support for Ubuntu 20.04.x, Ubuntu 22.04.x, RHEL 7.x, RHEL 8.x, RHEL 9.x, CentOS 7.x, Rocky Linux 8.x, Rocky Linux 9.x, SUSE SLES 15.x and OpenSUSE Leap 15.x will be until the standard EOSS as defined for each OS. Please refer to the support lifecycle for these OSes to know their support timelines.

  • CUDA supports the latest Debian release version. For Debian release timelines, visit https://wiki.debian.org/DebianReleases.

  • CUDA supports the latest Fedora release version. For Fedora release timelines, visit https://docs.fedoraproject.org/en-US/releases/.

  • CUDA supports a single KylinOS release version. For details, visit https://www.kylinos.cn/.

Refer to the support lifecycle for these supported OSes to know their support timelines and plan to move to newer releases accordingly.

1.3. Host Compiler Support Policy

In order to compile the CPU “Host” code in the CUDA source, the CUDA compiler NVCC requires a compatible host compiler to be installed on the system. The version of the host compiler supported on Linux platforms is tabulated as below. NVCC performs a version check on the host compiler’s major version and so newer minor versions of the compilers listed below will be supported, but major versions falling outside the range will not be supported.

Table 2 Supported Compilers

Distribution

GCC

Clang

NVHPC

XLC

ArmC/C++

ICC

x86_64

6.x - 12.2

7.x - 16.0

23.x

No

No

2021.7

Arm64 sbsa

6.x - 12.2

7.x - 16.0

22.x

No

22.1

No

POWER 9

6.x - 12.2

7.x - 16.0

22.x

16.1.x

No

No

For GCC and Clang, the preceding table indicates the minimum version and the latest version supported. If you are on a Linux distribution that may use an older version of GCC toolchain as default than what is listed above, it is recommended to upgrade to a newer toolchain CUDA 11.0 or later toolkit. Newer GCC toolchains are available with the Red Hat Developer Toolset for example. For platforms that ship a compiler version older than GCC 6 by default, linking to static or dynamic libraries that are shipped with the CUDA Toolkit is not supported. We only support libstdc++ (GCC’s implementation) for all the supported host compilers for the platforms listed above.

1.3.1. Supported C++ Dialects

NVCC and NVRTC (CUDA Runtime Compiler) support the following C++ dialect: C++11, C++14, C++17, C++20 on supported host compilers. The default C++ dialect of NVCC is determined by the default dialect of the host compiler used for compilation. Refer to host compiler documentation and the CUDA Programming Guide for more details on language support.

C++20 is supported with the following flavors of host compiler in both host and device code.

Distribution

GCC

Clang

NVHPC

Arm C/C++

x86_64

>=10.x

>=11.x

>=22.x

22.x

1.4. About This Document

This document is intended for readers familiar with the Linux environment and the compilation of C programs from the command line. You do not need previous experience with CUDA or experience with parallel computation. Note: This guide covers installation only on systems with X Windows installed.

Note

Many commands in this document might require superuser privileges. On most distributions of Linux, this will require you to log in as root. For systems that have enabled the sudo package, use the sudo prefix for all necessary commands.

2. Pre-installation Actions

Some actions must be taken before the CUDA Toolkit and Driver can be installed on Linux:

  • Verify the system has a CUDA-capable GPU.

  • Verify the system is running a supported version of Linux.

  • Verify the system has gcc installed.

  • Verify the system has the correct kernel headers and development packages installed.

  • Download the NVIDIA CUDA Toolkit.

  • Handle conflicting installation methods.

Note

You can override the install-time prerequisite checks by running the installer with the -override flag. Remember that the prerequisites will still be required to use the NVIDIA CUDA Toolkit.

2.1. Verify You Have a CUDA-Capable GPU

To verify that your GPU is CUDA-capable, go to your distribution’s equivalent of System Properties, or, from the command line, enter:

lspci | grep -i nvidia

If you do not see any settings, update the PCI hardware database that Linux maintains by entering update-pciids (generally found in /sbin) at the command line and rerun the previous lspci command.

If your graphics card is from NVIDIA and it is listed in https://developer.nvidia.com/cuda-gpus, your GPU is CUDA-capable.

The Release Notes for the CUDA Toolkit also contain a list of supported products.

2.2. Verify You Have a Supported Version of Linux

The CUDA Development Tools are only supported on some specific distributions of Linux. These are listed in the CUDA Toolkit release notes.

To determine which distribution and release number you’re running, type the following at the command line:

uname -m && cat /etc/*release

You should see output similar to the following, modified for your particular system:

x86_64
Red Hat Enterprise Linux Workstation release 6.0 (Santiago)

The x86_64 line indicates you are running on a 64-bit system. The remainder gives information about your distribution.

2.3. Verify the System Has gcc Installed

The gcc compiler is required for development using the CUDA Toolkit. It is not required for running CUDA applications. It is generally installed as part of the Linux installation, and in most cases the version of gcc installed with a supported version of Linux will work correctly.

To verify the version of gcc installed on your system, type the following on the command line:

gcc --version

If an error message displays, you need to install the development tools from your Linux distribution or obtain a version of gcc and its accompanying toolchain from the Web.

2.4. Verify the System has the Correct Kernel Headers and Development Packages Installed

The CUDA Driver requires that the kernel headers and development packages for the running version of the kernel be installed at the time of the driver installation, as well whenever the driver is rebuilt. For example, if your system is running kernel version 3.17.4-301, the 3.17.4-301 kernel headers and development packages must also be installed.

While the Runfile installation performs no package validation, the RPM and Deb installations of the driver will make an attempt to install the kernel header and development packages if no version of these packages is currently installed. However, it will install the latest version of these packages, which may or may not match the version of the kernel your system is using. Therefore, it is best to manually ensure the correct version of the kernel headers and development packages are installed prior to installing the CUDA Drivers, as well as whenever you change the kernel version.

The version of the kernel your system is running can be found by running the following command:

uname -r

This is the version of the kernel headers and development packages that must be installed prior to installing the CUDA Drivers. This command will be used multiple times below to specify the version of the packages to install. Note that below are the common-case scenarios for kernel usage. More advanced cases, such as custom kernel branches, should ensure that their kernel headers and sources match the kernel build they are running.

Note

If you perform a system update which changes the version of the Linux kernel being used, make sure to rerun the commands below to ensure you have the correct kernel headers and kernel development packages installed. Otherwise, the CUDA Driver will fail to work with the new kernel.

2.5. Install GPUDirect Storage

If you intend to use GPUDirectStorage (GDS), you must install the CUDA package and MLNX_OFED package.

GDS packages can be installed using the CUDA packaging guide. Follow the instructions in MLNX_OFED Requirements and Installation.

GDS is supported in two different modes: GDS (default/full perf mode) and Compatibility mode. Installation instructions for them differ slightly. Compatibility mode is the only mode that is supported on certain distributions due to software dependency limitations.

Full GDS support is restricted to the following Linux distros:

  • Ubuntu 20.04, Ubuntu 22.04

  • RHEL 8.3, RHEL 8.4, RHEL 9.0

Starting with CUDA toolkit 12.2.2, GDS kernel driver package nvidia-gds version 12.2.2-1 (provided by nvidia-fs-dkms 2.17.5-1) and above is only supported with the NVIDIA open kernel driver. Follow the instructions in Removing CUDA Toolkit and Driver to remove existing NVIDIA driver packages and then follow instructions in NVIDIA Open GPU Kernel Modules to install NVIDIA open kernel driver packages.

2.6. Choose an Installation Method

The CUDA Toolkit can be installed using either of two different installation mechanisms: distribution-specific packages (RPM and Deb packages), or a distribution-independent package (runfile packages).

The distribution-independent package has the advantage of working across a wider set of Linux distributions, but does not update the distribution’s native package management system. The distribution-specific packages interface with the distribution’s native package management system. It is recommended to use the distribution-specific packages, where possible.

Note

For both native as well as cross development, the toolkit must be installed using the distribution-specific installer. See the CUDA Cross-Platform Installation section for more details.

2.7. Download the NVIDIA CUDA Toolkit

The NVIDIA CUDA Toolkit is available at https://developer.nvidia.com/cuda-downloads.

Choose the platform you are using and download the NVIDIA CUDA Toolkit.

The CUDA Toolkit contains the CUDA driver and tools needed to create, build and run a CUDA application as well as libraries, header files, and other resources.

Download Verification

The download can be verified by comparing the MD5 checksum posted at https://developer.download.nvidia.com/compute/cuda/12.3.2/docs/sidebar/md5sum.txt with that of the downloaded file. If either of the checksums differ, the downloaded file is corrupt and needs to be downloaded again.

To calculate the MD5 checksum of the downloaded file, run the following:

md5sum <file>

2.8. Address Custom xorg.conf, If Applicable

The driver relies on an automatically generated xorg.conf file at /etc/X11/xorg.conf. If a custom-built xorg.conf file is present, this functionality will be disabled and the driver may not work. You can try removing the existing xorg.conf file, or adding the contents of /etc/X11/xorg.conf.d/00-nvidia.conf to the xorg.conf file. The xorg.conf file will most likely need manual tweaking for systems with a non-trivial GPU configuration.

2.9. Handle Conflicting Installation Methods

Before installing CUDA, any previous installations that could conflict should be uninstalled. This will not affect systems which have not had CUDA installed previously, or systems where the installation method has been preserved (RPM/Deb vs. Runfile). See the following charts for specifics.

Table 3 CUDA Toolkit Installation Compatibility Matrix

Installed Toolkit Version == X.Y

Installed Toolkit Version != X.Y

RPM/Deb

run

RPM/Deb

run

Installing Toolkit Version X.Y

RPM/Deb

No Action

Uninstall Run

No Action

No Action

run

Uninstall RPM/Deb

Uninstall Run

No Action

No Action

Table 4 NVIDIA Driver Installation Compatibility Matrix

Installed Driver Version == X.Y

Installed Driver Version != X.Y

RPM/Deb

run

RPM/Deb

run

Installing Driver Version X.Y

RPM/Deb

No Action

Uninstall Run

No Action

Uninstall Run

run

Uninstall RPM/Deb

No Action

Uninstall RPM/Deb

No Action

Use the following command to uninstall a Toolkit runfile installation:

sudo /usr/local/cuda-X.Y/bin/cuda-uninstaller

Use the following command to uninstall a Driver runfile installation:

sudo /usr/bin/nvidia-uninstall

Use the following commands to uninstall an RPM/Deb installation:

sudo dnf remove <package_name>                      # RHEL 8 / Rocky Linux 8
sudo yum remove <package_name>                      # RHEL7 / CentOS 7
sudo dnf remove <package_name>                      # Fedora
sudo zypper remove <package_name>                   # OpenSUSE / SLES
sudo apt-get --purge remove <package_name>          # Ubuntu

3. Package Manager Installation

Basic instructions can be found in the Quick Start Guide. Read on for more detailed instructions.

3.1. Overview

Installation using RPM or Debian packages interfaces with your system’s package management system. When using RPM or Debian local repo installers, the downloaded package contains a repository snapshot stored on the local filesystem in /var/. Such a package only informs the package manager where to find the actual installation packages, but will not install them.

If the online network repository is enabled, RPM or Debian packages will be automatically downloaded at installation time using the package manager: apt-get, dnf, yum, or zypper.

Distribution-specific instructions detail how to install CUDA:

Finally, some helpful package manager capabilities are detailed.

These instructions are for native development only. For cross-platform development, see the CUDA Cross-Platform Environment section.

Note

Optional components such as nvidia-fs, libnvidia_nscq, and fabricmanager are not installed by default and will have to be installed separately as needed.

3.2. RHEL 7 / CentOS 7

3.2.1. Prepare RHEL 7 / CentOS 7

  1. Perform the pre-installation actions.

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

    sudo yum install kernel-devel-$(uname -r) kernel-headers-$(uname -r)
    
  3. Satisfy third-party package dependency:

    • Satisfy DKMS dependency: The NVIDIA driver RPM packages depend on other external packages, such as DKMS and libvdpau. Those packages are only available on third-party repositories, such as EPEL. Any such third-party repositories must be added to the package manager repository database before installing the NVIDIA driver RPM packages, or missing dependencies will prevent the installation from proceeding.

      To enable EPEL:

      sudo yum install https://dl.fedoraproject.org/pub/epel/epel-release-latest-7.noarch.rpm
      
    • Enable optional repos:

      On RHEL 7 Linux only, execute the following steps to enable optional repositories.

      • On x86_64 workstation:

        subscription-manager repos --enable=rhel-7-workstation-optional-rpms
        
      • On POWER9 system:

        subscription-manager repos --enable=rhel-7-for-power-9-optional-rpms
        
      • On x86_64 server:

        subscription-manager repos --enable=rhel-7-server-optional-rpms
        
  4. Optional – Remove Outdated Signing Key:

    sudo rpm --erase gpg-pubkey-7fa2af80*
    
  5. Choose an installation method: local repo or network repo.

3.2.2. Local Repo Installation for RHEL 7 / CentOS 7

  1. Install local repository onto file system:

    sudo rpm --install cuda-repo-<distro>-X-Y-local-<version>*.<arch>.rpm
    

3.2.3. Network Repo Installation for RHEL 7 / CentOS 7

  1. Enable the network repo:

    sudo yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/$distro/$arch/cuda-$distro.repo
    

    where $distro/$arch should be replaced by one of the following:

    • rhel7/x86_64

    • rhel7/ppc64le

  2. Install the new CUDA public GPG key:

    The new GPG public key for the CUDA repository (RPM-based distros) is d42d0685.

    On a fresh installation of RHEL, the yum package manager will prompt the user to accept new keys when installing packages the first time. Indicate you accept the change when prompted.

    For upgrades, you must also also fetch an updated .repo entry:

    sudo yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/$distro/$arch/cuda-$distro.repo
    
  3. Clean Yum repository cache:

    sudo yum clean expire-cache
    

3.2.4. Common Installation Intructions for RHEL 7 / CentOS 7

These instructions apply to both local and network installation.

  1. Install CUDA SDK:

    sudo yum install nvidia-driver-latest-dkms
    sudo yum install cuda-toolkit
    sudo yum install cuda-drivers
    
  2. 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.

  3. Reboot the system:

    sudo reboot
    
  4. Perform the post-installation actions.

3.2.5. Installing a Previous NVIDIA Driver Branch on RHEL 7

To perform a network install of a previous NVIDIA driver branch on RHEL 7, use the commands below:

version=DRIVER_VERSION;
stream="latest-dkms";
list=("kmod-nvidia-$stream-$version")
list+=("nvidia-driver-$stream-cuda-$version")
list+=("nvidia-driver-$stream-cuda-libs-$version")
list+=("nvidia-driver-$stream-devel-$version")
list+=("nvidia-driver-$stream-$version")
list+=("nvidia-driver-$stream-NVML-$version")
list+=("nvidia-driver-$stream-NvFBCOpenGL-$version")
list+=("nvidia-driver-$stream-libs-$version")
list+=("nvidia-fabric-manager-$version")
list+=("nvidia-libXNVCtrl-$version")
list+=("nvidia-libXNVCtrl-devel-$version")
list+=("nvidia-modprobe-$stream-$version")
list+=("nvidia-persistenced-$stream-$version")
list+=("nvidia-settings-$version")
list+=("nvidia-xconfig-$stream-$version")
sudo yum --setopt=obsoletes=0  install ${list[@]}
sudo yum install cuda-drivers-fabricmanager-DRIVER_BRANCH

where:

  • DRIVER_VERSION is the full version, for example, 470.82.01

  • DRIVER_BRANCH is, for example, 470

  • KERNEL_STREAM is either “latest-dkms” for the historical proprietary installation, or “open-dkms” for the open GPU kernel module installation

3.3. RHEL 8 / Rocky 8

3.3.1. Prepare RHEL 8 / Rocky 8

  1. Perform the pre-installation actions.

  2. 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)
    

    If matching kernel-headers and kernel-devel packages are not available for the currently running kernel version, you may need to use the previously shipped version of these packages. See https://bugzilla.redhat.com/show_bug.cgi?id=1986132 for more information.

  3. Satisfy third-party package dependency:

    • Satisfy DKMS dependency: The NVIDIA driver RPM packages depend on other external packages, such as DKMS and libvdpau. Those packages are only available on third-party repositories, such as EPEL. Any such third-party repositories must be added to the package manager repository database before installing the NVIDIA driver RPM packages, or missing dependencies will prevent the installation from proceeding.

      To enable EPEL:

      sudo dnf install https://dl.fedoraproject.org/pub/epel/epel-release-latest-8.noarch.rpm
      
    • Enable optional repos:

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

      • On x86_64 systems:

        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
        
      • On POWER9 systems:

        subscription-manager repos --enable=rhel-8-for-ppc64le-appstream-rpms
        subscription-manager repos --enable=rhel-8-for-ppc64le-baseos-rpms
        subscription-manager repos --enable=codeready-builder-for-rhel-8-ppc64le-rpms
        
  4. Remove Outdated Signing Key:

    sudo rpm --erase gpg-pubkey-7fa2af80*
    
  5. Choose an installation method: local repo or network repo.

3.3.2. Local Repo Installation for RHEL 8 / Rocky 8

  1. Install local repository on file system:

sudo rpm --install cuda-repo-<distro>-X-Y-local-<version>*.<arch>.rpm

3.3.3. Network Repo Installation for RHEL 8 / Rocky 8

  1. Enable the network repo:

    sudo dnf config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/$distro/$arch/cuda-$distro.repo
    

    where $distro/$arch should be replaced by one of the following:

    • rhel8/cross-linux-sbsa

    • rhel8/ppc64le

    • rhel8/sbsa

    • rhel8/x86_64

  2. Install the new CUDA public GPG key:

    The new GPG public key for the CUDA repository (RPM-based distros) is d42d0685.

    On a fresh installation of RHEL, the dnf package manager will prompt the user to accept new keys when installing packages the first time. Indicate you accept the change when prompted.

    For upgrades, you must also also fetch an updated .repo entry:

    sudo dnf config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/$distro/$arch/cuda-$distro.repo
    
  3. Clean Yum repository cache:

    sudo dnf clean expire-cache
    

3.3.4. Common Instructions for RHEL 8 / Rocky 8

These instructions apply to both local and network installation.

  1. Install CUDA SDK:

    sudo dnf module install nvidia-driver:latest-dkms
    sudo dnf install cuda-toolkit
    
  2. Install GPUDirect Filesystem:

    sudo dnf install nvidia-gds
    
  3. 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.

  4. Reboot the system:

    sudo reboot
    
  5. Perform the post-installation actions.

3.4. RHEL 9 / Rocky 9

3.4.1. Prepare RHEL 9 / Rocky 9

  1. Perform the pre-installation actions.

  2. 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)
    
  3. Satisfy third-party package dependency:

    • Satisfy DKMS dependency: The NVIDIA driver RPM packages depend on other external packages, such as DKMS and libvdpau. Those packages are only available on third-party repositories, such as EPEL. Any such third-party repositories must be added to the package manager repository database before installing the NVIDIA driver RPM packages, or missing dependencies will prevent the installation from proceeding.

      To enable EPEL:

      sudo dnf install https://dl.fedoraproject.org/pub/epel/epel-release-latest-9.noarch.rpm
      
    • Enable optional repos:

      On RHEL 9 Linux only, execute the following steps to enable optional repositories.

      • On x86_64 systems:

        subscription-manager repos --enable=rhel-9-for-x86_64-appstream-rpms
        subscription-manager repos --enable=rhel-9-for-x86_64-baseos-rpms
        subscription-manager repos --enable=codeready-builder-for-rhel-9-x86_64-rpms
        
  4. Remove Outdated Signing Key:

    sudo rpm --erase gpg-pubkey-7fa2af80*
    
  5. Choose an installation method: local repo or network repo.

3.4.2. Local Repo Installation for RHEL 9 / Rocky 9

  1. Install local repository on file system:

    sudo rpm --install cuda-repo-<distro>-X-Y-local-<version>*.<arch>.rpm
    

3.4.3. Network Repo Installation for RHEL 9 / Rocky 9

  1. Enable the network repo:

    sudo dnf config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/$distro/$arch/cuda-$distro.repo
    

    where $distro/$arch should be replaced by one of the following:

    • rhel9/cross-linux-sbsa

    • rhel9/sbsa

    • rhel9/x86_64

  2. Install the new CUDA public GPG key:

    The new GPG public key for the CUDA repository (RPM-based distros) is d42d0685.

    On a fresh installation of RHEL, the dnf package manager will prompt the user to accept new keys when installing packages the first time. Indicate you accept the change when prompted.

    For upgrades, you must also also fetch an updated .repo entry:

    sudo dnf config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/$distro/$arch/cuda-$distro.repo
    
  3. Clean Yum repository cache:

    sudo dnf clean expire-cache
    

3.4.4. Common Instructions for RHEL 9 / Rocky 9

These instructions apply to both local and network installation.

  1. Install CUDA SDK:

    sudo dnf module install nvidia-driver:latest-dkms
    sudo dnf install cuda-toolkit
    
  2. Install GPUDirect Filesystem:

    sudo dnf install nvidia-gds
    
  3. 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.

  4. Reboot the system:

    sudo reboot
    
  5. Perform the post-installation actions.

3.5. KylinOS 10

3.5.1. Prepare KylinOS 10

  1. Perform the pre-installation actions.

  2. 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)
    
  3. Choose an installation method: local repo or network repo.

3.5.2. Local Repo Installation for KylinOS

  1. Install local repository on file system:

    sudo rpm --install cuda-repo-<distro>-X-Y-local-<version>*.<arch>.rpm
    

3.5.3. Network Repo Installation for KylinOS

  1. Enable the network repo:

    sudo dnf config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/kylin10/x86_64/cuda-$distro.repo
    
  2. Install the new CUDA public GPG key:

    The new GPG public key for the CUDA repository (RPM-based distros) is d42d0685.

    On a fresh installation of RHEL, the dnf package manager will prompt the user to accept new keys when installing packages the first time. Indicate you accept the change when prompted.

  3. Clean Yum repository cache:

    sudo dnf clean expire-cache
    

3.5.4. Common Instructions for KylinOS 10

These instructions apply to both local and network installation.

  1. Install CUDA SDK:

    sudo dnf module install nvidia-driver:latest-dkms
    sudo dnf install cuda-toolkit
    
  2. Install GPUDirect Filesystem:

    sudo dnf install nvidia-gds
    
  3. 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.

  4. Reboot the system:

    sudo reboot
    
  5. Perform the post-installation actions.

3.6. Fedora

3.6.1. Prepare Fedora

  1. Perform the pre-installation actions.

  2. 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)
    
  3. Remove Outdated Signing Key:

    sudo rpm --erase gpg-pubkey-7fa2af80*
    
  4. Choose an installation method: local repo or network repo.

3.6.2. Local Repo Installation for Fedora

  1. Install local repository on file system:

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

    where $distro is fedora33 or fedora35, for example.

3.6.3. Network Repo Installation for Fedora

  1. Enable the network repo:

    sudo dnf config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/$distro/x86_64/cuda-$distro.repo
    

    where $distro should be replaced by one of the following:

    • fedora32

    • fedora33

    • fedora34

    • fedora35

  2. Install the new CUDA public GPG key:

    The new GPG public key for the CUDA repository (RPM-based distros) is d42d0685.

    On a fresh installation of Fedora, the dnf package manager will prompt the user to accept new keys when installing packages the first time. Indicate you accept the change when prompted.

    For upgrades, you must also fetch an updated .repo entry:

    sudo dnf config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/$distro/x86_64/cuda-$distro.repo
    
  3. Clean DNF repository cache:

    sudo dnf clean expire-cache
    

3.6.4. Common Installation Intructions for Fedora

These instructions apply to both local and network installation for Fedora.

  1. Install CUDA SDK:

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

    Note

    The CUDA driver installation may fail if the RPMFusion non-free repository is enabled. In this case, CUDA installations should temporarily disable the RPMFusion non-free repository.

    sudo dnf --disablerepo="rpmfusion-nonfree*" install cuda
    

    It may be necessary to rebuild the grub configuration files, particularly if you use a non-default partition scheme. If so, then run this below command, and reboot the system:

    sudo grub2-mkconfig -o /boot/grub2/grub.cfg
    
  2. Reboot the system:

    sudo reboot
    
  3. 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.

  4. Perform the post-installation actions.

3.7. SLES

3.7.1. Prepare SLES

  1. Perform the pre-installation actions.

  2. The kernel development packages for the currently running kernel can be installed with:

    sudo zypper install -y kernel-<variant>-devel=<version>
    

    To run the above command, you will need the variant and version of the currently running kernel. Use the output of the uname command to determine the currently running kernel’s variant and version:

    $ uname -r
    3.16.6-2-default
    

    In the above example, the variant is default and version is 3.16.6-2.

    The kernel development packages for the default kernel variant can be installed with:

    sudo zypper install -y kernel-default-devel=$(uname -r | sed 's/\-default//')
    
  3. The kernel headers and development packages for the currently running kernel can be installed with:

    sudo zypper install -y kernel-<variant>-devel=<version>
    
  4. On SLES12 SP4, install the Mesa-libgl-devel Linux packages before proceeding. See Mesa-libGL-devel.

  5. Add the user to the video group:

    sudo usermod -a -G video <username>
    
  6. Remove Outdated Signing Key:

    sudo rpm --erase gpg-pubkey-7fa2af80*
    
  7. Choose an installation method: local repo or network repo.

3.7.2. Local Repo Installation for SLES

  1. Install local repository on file system:

sudo rpm --install cuda-repo-sles15-X-Y-local-<version>*.x86_64.rpm

3.7.3. Network Repo Installation for SLES

  1. Enable the network repo:

    sudo zypper addrepo https://developer.download.nvidia.com/compute/cuda/repos/$distro/$arch/cuda-$distro.repo
    

    where $distro/$arch should be replaced by one of the following:

    • sles15/cross-linux-sbsa

    • sles15/sbsa

    • sles15/x86_64

  2. Install the new CUDA public GPG key:

    The new GPG public key for the CUDA repository (RPM-based distros) is d42d0685.

    On a fresh installation of SLES, the zypper package manager will prompt the user to accept new keys when installing packages the first time. Indicate you accept the change when prompted.

    For upgrades, you must also also fetch an updated .repo entry:

    sudo zypper removerepo cuda-$distro-$arch
    sudo zypper addrepo https://developer.download.nvidia.com/compute/cuda/repos/$distro/$arch/cuda-$distro.repo
    
  3. Refresh Zypper repository cache:

    sudo SUSEConnect --product PackageHub/15/<architecture>
    sudo zypper refresh
    

3.7.4. Common Installation Instructions for SLES

These instructions apply to both local and network installation for SLES.

  1. Install CUDA SDK:

    sudo zypper install cuda-toolkit
    
  2. Install CUDA Samples GL dependencies:

    Refer to CUDA Cross-Platform Samples.

  3. Reboot the system:

    sudo reboot
    
  4. Perform the post-installation actions.

3.8. OpenSUSE

3.8.1. Prepare OpenSUSE

  1. Perform the pre-installation actions.

  2. The kernel development packages for the currently running kernel can be installed with:

    sudo zypper install -y kernel-<variant>-devel=<version>
    

    To run the above command, you will need the variant and version of the currently running kernel. Use the output of the uname command to determine the currently running kernel’s variant and version:

    $ uname -r
    3.16.6-2-default
    

    In the above example, the variant is default and version is 3.16.6-2.

    The kernel development packages for the default kernel variant can be installed with:

    sudo zypper install -y kernel-default-devel=$(uname -r | sed 's/\-default//')
    
  3. Add the user to the video group:

    sudo usermod -a -G video <username>
    
  4. Remove Outdated Signing Key:

    sudo rpm --erase gpg-pubkey-7fa2af80*
    
  5. Choose an installation method: local repo or network repo.

3.8.2. Local Repo Installation for OpenSUSE

  1. Install local repository on file system:

    sudo rpm --install cuda-repo-opensuse15-<version>.x86_64.rpm
    

3.8.3. Network Repo Installation for OpenSUSE

  1. Enable the network repo:

    sudo zypper addrepo https://developer.download.nvidia.com/compute/cuda/repos/opensuse15/x86_64/cuda-opensuse15.repo
    
  2. Install the new CUDA public GPG key:

    The new GPG public key for the CUDA repository (RPM-based distros) is d42d0685. On fresh installation of openSUSE, the zypper package manager will prompt the user to accept new keys when installing packages the first time. Indicate you accept the change when prompted.

    For upgrades, you must also also fetch an updated .repo entry:

    sudo zypper removerepo cuda-opensuse15-x86_64
    sudo zypper addrepo https://developer.download.nvidia.com/compute/cuda/repos/opensuse15/x86_64/cuda-opensuse15.repo
    
  3. Refresh Zypper repository cache:

    sudo zypper refresh
    

3.8.4. Common Installation Instructions for OpenSUSE

These instructions apply to both local and network installation for OpenSUSE.

  1. Install CUDA SDK:

    sudo zypper install cuda-toolkit
    
  2. Reboot the system:

    sudo reboot
    
  3. Perform the post-installation actions.

3.9. WSL

These instructions must be used if you are installing in a WSL environment. Do not use the Ubuntu instructions in this case; it is important to not install the cuda-drivers packages within the WSL environment.

3.9.1. Prepare WSL

  1. Perform the pre-installation actions.

  2. Remove Outdated Signing Key:

    sudo apt-key del 7fa2af80
    
  3. Choose an installation method: local repo or network repo.

3.9.2. Local Repo Installation for WSL

  1. Install local repositiry on file system:

    sudo dpkg -i cuda-repo-wsl-ubuntu-X-Y-local_<version>*_x86_64.deb
    
  2. Enroll ephemeral public GPG key:

    sudo cp /var/cuda-repo-wsl-ubuntu-X-Y-local/cuda-*-keyring.gpg /usr/share/keyrings/
    

3.9.3. Network Repo Installation for WSL

The new GPG public key for the CUDA repository (Debian-based distros) is 3bf863cc. This must be enrolled on the system, either using the cuda-keyring package or manually; the apt-key command is deprecated and not recommended.

  1. Install the newcuda-keyring package:

    wget https://developer.download.nvidia.com/compute/cuda/repos/wsl-ubuntu/x86_64/cuda-keyring_1.1-1_all.deb
    sudo dpkg -i cuda-keyring_1.1-1_all.deb
    

    Or if you are unable to install the cuda-keyring package, you can optionally:

    1. Enroll the new signing key manually:

      wget https://developer.download.nvidia.com/compute/cuda/repos/wsl-ubuntu/x86_64/cuda-archive-keyring.gpg
      sudo mv cuda-archive-keyring.gpg /usr/share/keyrings/cuda-archive-keyring.gpg
      
    2. Enable the network repository:

      echo "deb [signed-by=/usr/share/keyrings/cuda-archive-keyring.gpg] https://developer.download.nvidia.com/compute/cuda/repos/wsl-ubuntu/x86_64/ /" | sudo tee /etc/apt/sources.list.d/cuda-wsl-ubuntu-x86_64.list
      
    3. Add pin file to prioritize CUDA repository:

      wget https://developer.download.nvidia.com/compute/cuda/repos/wsl-ubuntu/x86_64/cuda-wsl-ubuntu.pin
      sudo mv cuda-wsl-ubuntu.pin /etc/apt/preferences.d/cuda-repository-pin-600
      

3.9.4. Common Installation Instructions for WSL

These instructions apply to both local and network installation for WSL.

  1. Update the Apt repository cache:

    sudo apt-get update
    
  2. Install CUDA SDK:

    sudo apt-get install cuda-toolkit
    
  3. Perform the post-installation actions.

3.10. Ubuntu

3.10.1. Prepare Ubuntu

  1. Perform the pre-installation actions.

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

    sudo apt-get install linux-headers-$(uname -r)
    
  3. Remove Outdated Signing Key:

    sudo apt-key del 7fa2af80
    
  4. Choose an installation method: local repo or network repo.

3.10.2. Local Repo Installation for Ubuntu

  1. Install local repository on file system:

    sudo dpkg -i cuda-repo-<distro>_<version>_<architecture>.deb
    
  2. Enroll ephemeral public GPG key:

    sudo cp /var/cuda-repo-<distro>-X-Y-local/cuda-*-keyring.gpg /usr/share/keyrings/
    
  3. Add pin file to prioritize CUDA repository:

    wget https://developer.download.nvidia.com/compute/cuda/repos/<distro>/x86_64/cuda-<distro>.pin
     sudo mv cuda-<distro>.pin /etc/apt/preferences.d/cuda-repository-pin-600
    

3.10.3. Network Repo Installation for Ubuntu

The new GPG public key for the CUDA repository is 3bf863cc. This must be enrolled on the system, either using the cuda-keyring package or manually; the apt-key command is deprecated and not recommended.

  1. Install the new cuda-keyring package:

    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
    

    where $distro/$arch should be replaced by one of the following:

    • ubuntu1604/x86_64

    • ubuntu1804/cross-linux-sbsa

    • ubuntu1804/ppc64el

    • ubuntu1804/sbsa

    • ubuntu1804/x86_64

    • ubuntu2004/cross-linux-aarch64

    • ubuntu2004/arm64

    • ubuntu2004/cross-linux-sbsa

    • ubuntu2004/sbsa

    • ubuntu2004/x86_64

    • ubuntu2204/sbsa

    • ubuntu2204/x86_64

    Note

    arm64-Jetson repos:

    • native: $distro/arm64

    • cross: $distro/cross-linux-aarch64

    sudo dpkg -i cuda-keyring_1.1-1_all.deb
    
  2. Or if you are unable to install the cuda-keyring package, you can optionally:

    1. Enroll the new signing key manually:

      wget https://developer.download.nvidia.com/compute/cuda/repos/<distro>/<arch>/cuda-archive-keyring.gpg
      sudo mv cuda-archive-keyring.gpg /usr/share/keyrings/cuda-archive-keyring.gpg
      
    2. Enable the network repository:

      echo "deb [signed-by=/usr/share/keyrings/cuda-archive-keyring.gpg] https://developer.download.nvidia.com/compute/cuda/repos/<distro>/<arch>/ /" | sudo tee /etc/apt/sources.list.d/cuda-<distro>-<arch>.list
      
    3. Add pin file to prioritize CUDA repository:

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

3.10.4. Common Installation Instructions for Ubuntu

These instructions apply to both local and network installation for Ubuntu.

  1. Update the Apt repository cache:

    sudo apt-get update
    
  2. Install CUDA SDK:

    Note

    These two commands must be executed separately.

    sudo apt-get install cuda-toolkit
    

    To include all GDS packages:

    sudo apt-get install nvidia-gds
    
  3. Reboot the system

    sudo reboot
    
  4. Perform the Post-installation Actions

3.11. Debian

3.11.1. Prepare Debian

  1. Perform the pre-installation actions.

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

    sudo apt-get install linux-headers-$(uname -r)
    
  3. Enable the contrib repository:

    sudo add-apt-repository contrib
    
  4. Remove Outdated Signing Key:

    sudo apt-key del 7fa2af80
    
  5. Choose an installation method: local repo or network repo.

3.11.2. Local Repo Installation for Debian

  1. Install local repository on file system:

    sudo dpkg -i cuda-repo-<distro>-X-Y-local_<version>*_x86_64.deb
    
  2. Enroll ephemeral public GPG key:

    sudo cp /var/cuda-repo-<distro>-X-Y-local/cuda-*-keyring.gpg /usr/share/keyrings/
    

3.11.3. Network Repo Installation for Debian

The new GPG public key for the CUDA repository (Debian-based distros) is 3bf863cc. This must be enrolled on the system, either using the cuda-keyring package or manually; the apt-key command is deprecated and not recommended.

  1. Install the new cuda-keyring package:

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

    where $distro/$arch should be replaced by one of the following:

    • debian10/x86_64

    • debian11/x86_64

    sudo dpkg -i cuda-keyring_1.1-1_all.deb
    
  2. Or if you are unable to install the cuda-keyring package, you can optionally:

    1. Enroll the new signing key manually:

      wget https://developer.download.nvidia.com/compute/cuda/repos/<distro>/x86_64/cuda-archive-keyring.gpg
      sudo mv cuda-archive-keyring.gpg /usr/share/keyrings/cuda-archive-keyring.gpg
      
    2. Enable the network repository:

      echo "deb [signed-by=/usr/share/keyrings/cuda-archive-keyring.gpg] https://developer.download.nvidia.com/compute/cuda/repos/<distro>/x86_64/ /" | sudo tee /etc/apt/sources.list.d/cuda-<distro>-x86_64.list
      

3.11.4. Common Installation Instructions for Debian

These instructions apply to both local and network installation for Debian.

  1. Update the Apt repository cache:

    sudo apt-get update
    

    Note

    If you are using Debian 10, you may instead need to run:

    sudo apt-get --allow-releaseinfo-change update
    
  2. Install CUDA SDK:

    sudo apt-get -y install cuda
    
  3. Reboot the system:

    sudo reboot
    
  4. Perform the post-installation actions.

3.12. Additional Package Manager Capabilities

Below are some additional capabilities of the package manager that users can take advantage of.

3.12.1. Available Packages

The recommended installation package is the cuda package. This package will install the full set of other CUDA packages required for native development and should cover most scenarios.

The cuda package installs all the available packages for native developments. That includes the compiler, the debugger, the profiler, the math libraries, and so on. For x86_64 platforms, this also includes Nsight Eclipse Edition and the visual profilers. It also includes the NVIDIA driver package.

On supported platforms, the cuda-cross-aarch64 and cuda-cross-ppc64el packages install all the packages required for cross-platform development to ARMv8 and POWER8, respectively. The libraries and header files of the target architecture’s display driver package are also installed to enable the cross compilation of driver applications. The cuda-cross-<arch> packages do not install the native display driver.

Note

32-bit compilation native and cross-compilation is removed from CUDA 12.0 and later Toolkit. Use the CUDA Toolkit from earlier releases for 32-bit compilation. CUDA Driver will continue to support running existing 32-bit applications on existing GPUs except Hopper. Hopper does not support 32-bit applications. Ada will be the last architecture with driver support for 32-bit applications.

The packages installed by the packages above can also be installed individually by specifying their names explicitly. The list of available packages be can obtained with:

yum --disablerepo="*" --enablerepo="cuda*" list available    # RedHat
dnf --disablerepo="*" --enablerepo="cuda*" list available    # Fedora
zypper packages -r cuda                                      # OpenSUSE & SLES
cat /var/lib/apt/lists/*cuda*Packages | grep "Package:"      # Ubuntu

3.12.2. Meta Packages

Meta packages are RPM/Deb/Conda packages which contain no (or few) files but have multiple dependencies. They are used to install many CUDA packages when you may not know the details of the packages you want. The following table lists the meta packages.

Table 5 Meta Packages Available for CUDA 12.3

Meta Package

Purpose

cuda

Installs all CUDA Toolkit and Driver packages. Handles upgrading to the next version of the cuda package when it’s released.

cuda-12-3

Installs all CUDA Toolkit and Driver packages. Remains at version 12.3 until an additional version of CUDA is installed.

cuda-toolkit-12-3

Installs all CUDA Toolkit packages required to develop CUDA applications. Does not include the driver.

cuda-toolkit-12

Installs all CUDA Toolkit packages required to develop applications. Will not upgrade beyond the 12.x series toolkits. Does not include the driver.

cuda-toolkit

Installs all CUDA Toolkit packages required to develop applications. Handles upgrading to the next 12.x version of CUDA when it’s released. Does not include the driver.

cuda-tools-12-3

Installs all CUDA command line and visual tools.

cuda-runtime-12-3

Installs all CUDA Toolkit packages required to run CUDA applications, as well as the Driver packages.

cuda-compiler-12-3

Installs all CUDA compiler packages.

cuda-libraries-12-3

Installs all runtime CUDA Library packages.

cuda-libraries-dev-12-3

Installs all development CUDA Library packages.

cuda-drivers

Installs all Driver packages. Handles upgrading to the next version of the Driver packages when they’re released.

3.12.3. Optional 32-bit Packages for Linux x86_64 .deb/.rpm

These packages provide 32-bit driver libraries needed for things such as Steam (popular game app store/launcher), older video games, and some compute applications.

For Debian 11:

sudo dpkg --add-architecture i386
sudo apt-get update
sudo apt-get install libcuda1-i386 nvidia-driver-libs-i386

For Ubuntu:

sudo dpkg --add-architecture i386
sudo apt-get update
sudo apt-get install libnvidia-compute-<branch>:i386 libnvidia-decode-<branch>:i386 \
 libnvidia-encode-<branch>:i386 libnvidia-extra-<branch>:i386 libnvidia-fbc1-<branch>:i386 \
 libnvidia-gl-<branch>:i386

Where <branch> is the driver version, for example 495.

For Fedora and RHEL8:

sudo dnf install nvidia-driver-cuda-libs.i686 nvidia-driver-devel.i686 \
 nvidia-driver-libs.i686 nvidia-driver-NvFBCOpenGL.i686 nvidia-driver-NVML.i686

Note

There is no modularity profile support.

For openSUSE/SLES:

No extra installation is required, the nvidia-glG05 package already contains the 32-bit libraries.

3.12.4. Package Upgrades

The cuda package points to the latest stable release of the CUDA Toolkit. When a new version is available, use the following commands to upgrade the toolkit and driver:

sudo yum install cuda-toolkit                                # RHEL7
sudo dnf install cuda-toolkit                                # Fedora, RHEL9, RHEL8, and KylinOS
sudo zypper install cuda-toolkit                             # OpenSUSE and SLES
sudo apt-get install cuda-toolkit                            # Ubuntu and Debian

The cuda-cross-<arch> packages can also be upgraded in the same manner.

The cuda-drivers package points to the latest driver release available in the CUDA repository. When a new version is available, use the following commands to upgrade the driver:

sudo yum install nvidia-driver-latest-dkms                   # RHEL7
sudo yum install cuda-drivers
sudo dnf module install nvidia-driver:latest-dkms             # Fedora, RHEL9, RHEL8, and KylinOS
sudo zypper install cuda-drivers nvidia-gfxG04-kmp-default   # OpenSUSE and SLES
sudo apt-get install cuda-drivers                            # Ubuntu and Debian

Some desktop environments, such as GNOME or KDE, will display a notification alert when new packages are available.

To avoid any automatic upgrade, and lock down the toolkit installation to the X.Y release, install the cuda-X-Y or cuda-cross-<arch>-X-Y package.

Side-by-side installations are supported. For instance, to install both the X.Y CUDA Toolkit and the X.Y+1 CUDA Toolkit, install the cuda-X.Y and cuda-X.Y+1 packages.

4. Driver Installation

This section is for users who want to install a specific driver version.

For Debian and Ubuntu:

sudo apt-get install cuda-drivers-<branch>

For example:

sudo apt-get install cuda-drivers-418

For OpenSUSE and SLES:

sudo zypper install cuda-drivers-<branch>

For example:

sudo zypper install cuda-drivers-450

This allows you to get the highest version in the specified branch.

For Fedora and RHEL8:

sudo dnf module install nvidia-driver:<stream>/<profile>

where profile by default is “default” and does not need to be specified.

  • Example dkms streams: 450-dkms or latest-dkms

  • Example precompiled streams: 450 or latest

Note

Precompiled streams are only supported on RHEL8 x86_64 and RHEL9 x86_64.

To uninstall or change streams on Fedora and RHEL8:

sudo dnf module remove --all nvidia-driver
sudo dnf module reset nvidia-driver

5. NVIDIA Open GPU Kernel Modules

The NVIDIA Linux GPU Driver contains several kernel modules:

  • nvidia.ko

  • nvidia-modeset.ko

  • nvidia-uvm.ko

  • nvidia-drm.ko

  • nvidia-peermem.ko

Starting in the 515 driver release series, two “flavors” of these kernel modules are provided:

Verify that your NVIDIA GPU is at least Turing or newer generation.

lspci | grep VGA

Experimental support for GeForce and Quadro SKUs can be enabled with:

echo "options nvidia NVreg_OpenRmEnableUnsupportedGpus=1" | sudo tee /etc/modprobe.d/nvidia-gsp.conf

To install NVIDIA Open GPU Kernel Modules, follow the instructions below.

5.1. CUDA Runfile

Pass the CLI argument to the CUDA runfile to opt in to NVIDIA Open GPU Kernel Modules:

sh cuda_<release>_<version>_linux.run -m=kernel-open

5.2. Debian

  1. Install the NVIDIA Open GPU Kernel Modules package:

    sudo apt-get install nvidia-kernel-open-dkms
    
  2. Install the rest of the NVIDIA driver packages:

    sudo apt-get install cuda-drivers
    

5.3. Fedora

  1. Install the NVIDIA Open GPU Kernel Modules package and the rest of the NVIDIA driver packages:

    sudo dnf module install nvidia-driver:open-dkms
    

5.4. KylinOS 10

  1. Install the NVIDIA Open GPU Kernel Modules package and the rest of the NVIDIA driver packages:

    sudo dnf module install nvidia-driver:open-dkms
    

5.5. RHEL 9 and Rocky 9

  1. Install the NVIDIA Open GPU Kernel Modules package and the rest of the NVIDIA driver packages:

    sudo dnf module install nvidia-driver:open-dkms
    

5.6. RHEL 8 and Rocky 8

  1. Install the NVIDIA Open GPU Kernel Modules package and the rest of the NVIDIA driver packages:

    sudo dnf module install nvidia-driver:open-dkms
    

5.7. RHEL 7 and CentOS 7

  1. Install the NVIDIA Open GPU Kernel Modules package:

    sudo yum install kmod-nvidia-open-dkms
    
  2. Install the rest of the NVIDIA driver packages (except nvidia-settings):

    sudo yum install nvidia-driver-latest-dkms
    
  3. Install nvidia-settings:

    sudo yum install cuda-drivers
    

5.8. OpenSUSE and SLES

  1. Install the NVIDIA Open GPU Kernel Modules package:

    sudo zypper install nvidia-open-gfxG05-kmp-default
    
  2. Install the rest of the NVIDIA driver packages:

    sudo zypper install cuda-drivers
    

5.9. Ubuntu

  1. Install the NVIDIA Open GPU Kernel Modules package:

    sudo apt-get install nvidia-kernel-open-520
    
  2. Install the rest of the NVIDIA driver packages:

    sudo apt-get install cuda-drivers-520
    

6. Precompiled Streams

Precompiled streams offer an optional method of streamlining the installation process.

The advantages of precompiled streams:

  • Precompiled: faster boot up after driver and/or kernel updates

  • Pre-tested: kernel and driver combination has been validated

  • Removes gcc dependency: no compiler installation required

  • Removes dkms dependency: enabling EPEL repository not required

  • Removes kernel-devel and kernel-headers dependencies: no black screen if matching packages are missing

When using precompiled drivers, a plugin for the dnf package manager is enabled that cleans up stale .ko files. To prevent system breakages, the NVIDIA dnf plugin also prevents upgrading to a kernel for which no precompiled driver yet exists. This can delay the application of security fixes but ensures that a tested kernel and driver combination is always used. A warning is displayed by dnf during that upgrade situation:

NOTE:  Skipping kernel installation since no NVIDIA driver kernel module package
 kmod-nvidia-${driver}-${kernel} ... could be found

Packaging templates and instructions are provided on GitHub to allow you to maintain your own precompiled kernel module packages for custom kernels and derivative Linux distros: NVIDIA/yum-packaging-precompiled-kmod

To use the new driver packages on RHEL 8 or RHEL 9:

  1. First, ensure that the Red Hat repositories are enabled:

    RHEL 8:

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

    or

    RHEL 9:

    subscription-manager repos --enable=rhel-9-for-x86_64-appstream-rpms
    subscription-manager repos --enable=rhel-9-for-x86_64-baseos-rpms
    
  2. Choose one of the four options below depending on the desired driver:

    • latest always updates to the highest versioned driver (precompiled):

      sudo dnf module install nvidia-driver:latest
      
    • <id> locks the driver updates to the specified driver branch (precompiled):

      sudo dnf module install nvidia-driver:<id>
      

      Note

      Replace <id> with the appropriate driver branch streams, for example 520, 515, 470, or 450.

    • latest-dkms always updates to the highest versioned driver (non-precompiled):

      sudo dnf module install nvidia-driver:latest-dkms
      

      Note

      This is the default stream.

    • <id>-dkms locks the driver updates to the specified driver branch (non-precompiled):

      sudo dnf module install nvidia-driver:<id>-dkms
      

      Note

      Valid streams include 520-dkms, 515-dkms, 470-dkms, and 450-dkms.

6.1. Precompiled Streams Support Matrix

This table shows the supported precompiled and legacy DKMS streams for each driver.

NVIDIA Driver

Precompiled Stream

Legacy DKMS Stream

Open DKMS Stream

Highest version

latest

latest-dkms

open-dkms

Locked at 520.x

520

520-dkms

520-open

Locked at 515.x

515

515-dkms

515-open

Prior to switching between module streams, first reset:

sudo dnf module reset nvidia-driver

Note

This is also required for upgrading between branch locked streams.

Or alternatively:

sudo dnf module switch-to nvidia-driver:<stream>

6.2. Modularity Profiles

Modularity profiles work with any supported modularity stream and allow for additional use cases. These modularity profiles are available on RHEL8 and Fedora.

Table 6 List of nvidia-driver Module Profiles

Stream

Profile

Use Case

Default

/default

Installs all the driver packages in a stream.

Kickstart

/ks

Performs unattended Linux OS installation using a config file.

NVSwitch Fabric

/fm

Installs all the driver packages plus components required for bootstrapping an NVSwitch system (including the Fabric Manager and NSCQ telemetry).

Source

/src

Source headers for compilation (precompiled streams only).

For example:

sudo dnf module nvidia-driver:<stream>/default
sudo dnf module nvidia-driver:<stream>/ks
sudo dnf module nvidia-driver:<stream>/fm
sudo dnf module nvidia-driver:<stream>/src

You can install multiple modularity profiles using BASH curly brace expansion, for example:

sudo dnf module install nvidia-driver:latest/{default,src}

See https://developer.nvidia.com/blog/streamlining-nvidia-driver-deployment-on-rhel-8-with-modularity-streams in the Developer Blog and https://developer.download.nvidia.com/compute/cuda/repos/rhel8/x86_64/precompiled/ for more information.

7. Kickstart Installation

7.1. RHEL 8 / Rocky Linux 8

  1. Enable the EPEL repository:

    repo --name=epel --baseurl=http://download.fedoraproject.org/pub/epel/8/Everything/x86_64/
    
  2. Enable the CUDA repository:

    repo --name=cuda-rhel8 --baseurl=https://developer.download.nvidia.com/compute/cuda/repos/rhel8/x86_64/
    
  3. In the packages section of the ks.cfg file, make sure you are using the /ks profile and :latest-dkms stream:

    @nvidia-driver:latest-dkms/ks
    
  4. Perform the post-installation actions.

7.2. RHEL 9 / Rocky Linux 9

  1. Enable the EPEL repository:

    repo --name=epel --baseurl=http://download.fedoraproject.org/pub/epel/9/Everything/x86_64/
    
  2. Enable the CUDA repository:

    repo --name=cuda-rhel9 --baseurl=https://developer.download.nvidia.com/compute/cuda/repos/rhel9/x86_64/
    
  3. In the packages section of the ks.cfg file, make sure you are using the /ks profile and :latest-dkms stream:

    @nvidia-driver:latest-dkms/ks
    
  4. Perform the post-installation actions.

8. Runfile Installation

Basic instructions can be found in the Quick Start Guide. Read on for more detailed instructions.

This section describes the installation and configuration of CUDA when using the standalone installer. The standalone installer is a “.run” file and is completely self-contained.

8.1. Runfile Overview

The Runfile installation installs the NVIDIA Driver and CUDA Toolkit via an interactive ncurses-based interface.

The installation steps are listed below. Distribution-specific instructions on disabling the Nouveau drivers as well as steps for verifying device node creation are also provided.

Finally, advanced options for the installer and uninstallation steps are detailed below.

The Runfile installation does not include support for cross-platform development. For cross-platform development, see the CUDA Cross-Platform Environment section.

8.2. Installation

  1. Perform the pre-installation actions.

  2. Disable the Nouveau drivers.

  3. Reboot into text mode (runlevel 3).

    This can usually be accomplished by adding the number “3” to the end of the system’s kernel boot parameters.

    Since the NVIDIA drivers are not yet installed, the text terminals may not display correctly. Temporarily adding “nomodeset” to the system’s kernel boot parameters may fix this issue.

    Consult your system’s bootloader documentation for information on how to make the above boot parameter changes.

    The reboot is required to completely unload the Nouveau drivers and prevent the graphical interface from loading. The CUDA driver cannot be installed while the Nouveau drivers are loaded or while the graphical interface is active.

  4. Verify that the Nouveau drivers are not loaded. If the Nouveau drivers are still loaded, consult your distribution’s documentation to see if further steps are needed to disable Nouveau.

  5. Run the installer and follow the on-screen prompts:

    sudo sh cuda_<version>_linux.run
    

    The installer will prompt for the following:

    • EULA Acceptance

    • CUDA Driver installation

    • CUDA Toolkit installation, location, and /usr/local/cuda symbolic link

    The default installation location for the toolkit is /usr/local/cuda-12.0:

    The /usr/local/cuda symbolic link points to the location where the CUDA Toolkit was installed. This link allows projects to use the latest CUDA Toolkit without any configuration file update.

    The installer must be executed with sufficient privileges to perform some actions. When the current privileges are insufficient to perform an action, the installer will ask for the user’s password to attempt to install with root privileges. Actions that cause the installer to attempt to install with root privileges are:

    • installing the CUDA Driver

    • installing the CUDA Toolkit to a location the user does not have permission to write to

    • creating the /usr/local/cuda symbolic link

    Running the installer with sudo, as shown above, will give permission to install to directories that require root permissions. Directories and files created while running the installer with sudo will have root ownership.

    If installing the driver, the installer will also ask if the openGL libraries should be installed. If the GPU used for display is not an NVIDIA GPU, the NVIDIA openGL libraries should not be installed. Otherwise, the openGL libraries used by the graphics driver of the non-NVIDIA GPU will be overwritten and the GUI will not work. If performing a silent installation, the --no-opengl-libs option should be used to prevent the openGL libraries from being installed. See the Advanced Options section for more details.

    If the GPU used for display is an NVIDIA GPU, the X server configuration file, /etc/X11/xorg.conf, may need to be modified. In some cases, nvidia-xconfig can be used to automatically generate an xorg.conf file that works for the system. For non-standard systems, such as those with more than one GPU, it is recommended to manually edit the xorg.conf file. Consult the xorg.conf documentation for more information.

    Note

    Installing Mesa may overwrite the /usr/lib/libGL.so that was previously installed by the NVIDIA driver, so a reinstallation of the NVIDIA driver might be required after installing these libraries.

  6. Reboot the system to reload the graphical interface:

    sudo reboot
    
  7. Verify the device nodes are created properly.

  8. Perform the post-installation actions.

8.3. Disabling Nouveau

To install the Display Driver, the Nouveau drivers must first be disabled. Each distribution of Linux has a different method for disabling Nouveau.

The Nouveau drivers are loaded if the following command prints anything:

lsmod | grep nouveau

8.3.1. Fedora

  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 following command:

    sudo grub2-mkconfig -o /boot/grub2/grub.cfg
    
  4. Reboot the system.

8.3.2. RHEL / Rocky and KylinOS

  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
    

8.3.3. OpenSUSE

  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
    

8.3.4. SLES

No actions to disable Nouveau are required as Nouveau is not installed on SLES.

8.3.5. WSL

No actions to disable Nouveau are required as Nouveau is not installed on WSL.

8.3.6. Ubuntu

  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
    

8.3.7. Debian

  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
    

8.4. Device Node Verification

Check that the device files/dev/nvidia* exist and have the correct (0666) file permissions. These files are used by the CUDA Driver to communicate with the kernel-mode portion of the NVIDIA Driver. Applications that use the NVIDIA driver, such as a CUDA application or the X server (if any), will normally automatically create these files if they are missing using the setuidnvidia-modprobe tool that is bundled with the NVIDIA Driver. However, some systems disallow setuid binaries, so if these files do not exist, you can create them manually by using a startup script such as the one below:

#!/bin/bash

/sbin/modprobe nvidia

if [ "$?" -eq 0 ]; then
  # Count the number of NVIDIA controllers found.
  NVDEVS=`lspci | grep -i NVIDIA`
  N3D=`echo "$NVDEVS" | grep "3D controller" | wc -l`
  NVGA=`echo "$NVDEVS" | grep "VGA compatible controller" | wc -l`

  N=`expr $N3D + $NVGA - 1`
  for i in `seq 0 $N`; do
    mknod -m 666 /dev/nvidia$i c 195 $i
  done

  mknod -m 666 /dev/nvidiactl c 195 255

else
  exit 1
fi

/sbin/modprobe nvidia-uvm

if [ "$?" -eq 0 ]; then
  # Find out the major device number used by the nvidia-uvm driver
  D=`grep nvidia-uvm /proc/devices | awk '{print $1}'`

  mknod -m 666 /dev/nvidia-uvm c $D 0
else
  exit 1
fi

8.5. Advanced Options

Action

Options Used

Explanation

Silent Installation

--silent

Required for any silent installation. Performs an installation with no further user-input and minimal command-line output based on the options provided below. Silent installations are useful for scripting the installation of CUDA. Using this option implies acceptance of the EULA. The following flags can be used to customize the actions taken during installation. At least one of --driver, --uninstall, and --toolkit must be passed if running with non-root permissions.

--driver

Install the CUDA Driver.

--toolkit

Install the CUDA Toolkit.

--toolkitpath=<path>

Install the CUDA Toolkit to the <path> directory. If not provided, the default path of /usr/local/cuda-12.0 is used.

--defaultroot=<path>

Install libraries to the <path> directory. If the <path> is not provided, then the default path of your distribution is used. This only applies to the libraries installed outside of the CUDA Toolkit path.

Extraction

--extract=<path>

Extracts to the <path> the following: the driver runfile, the raw files of the toolkit to <path>.

This is especially useful when one wants to install the driver using one or more of the command-line options provided by the driver installer which are not exposed in this installer.

Overriding Installation Checks

--override

Ignores compiler, third-party library, and toolkit detection checks which would prevent the CUDA Toolkit from installing.

No OpenGL Libraries

--no-opengl-libs

Prevents the driver installation from installing NVIDIA’s GL libraries. Useful for systems where the display is driven by a non-NVIDIA GPU. In such systems, NVIDIA’s GL libraries could prevent X from loading properly.

No man pages

--no-man-page

Do not install the man pages under /usr/share/man.

Overriding Kernel Source

--kernel-source-path=<path>

Tells the driver installation to use <path> as the kernel source directory when building the NVIDIA kernel module. Required for systems where the kernel source is installed to a non-standard location.

Running nvidia-xconfig

--run-nvidia-xconfig

Tells the driver installation to run nvidia-xconfig to update the system X configuration file so that the NVIDIA X driver is used. The pre-existing X configuration file will be backed up.

No nvidia-drm kernel module

--no-drm

Do not install the nvidia-drm kernel module. This option should only be used to work around failures to build or install the nvidia-drm kernel module on systems that do not need the provided features.

Custom Temporary Directory Selection

--tmpdir=<path>

Performs any temporary actions within <path> instead of /tmp. Useful in cases where /tmp cannot be used (doesn’t exist, is full, is mounted with ‘noexec’, etc.).

Show Installer Options

--help

Prints the list of command-line options to stdout.

8.6. Uninstallation

To uninstall the CUDA Toolkit, run the uninstallation script provided in the bin directory of the toolkit. By default, it is located in /usr/local/cuda-12.0/bin:

sudo /usr/local/cuda-12.0/bin/cuda-uninstaller

To uninstall the NVIDIA Driver, run nvidia-uninstall:

sudo /usr/bin/nvidia-uninstall

To enable the Nouveau drivers, remove the blacklist file created in the Disabling Nouveau section, and regenerate the kernel initramfs/initrd again as described in that section.

9. Conda Installation

This section describes the installation and configuration of CUDA when using the Conda installer. The Conda packages are available at https://anaconda.org/nvidia.

9.1. Conda Overview

The Conda installation installs the CUDA Toolkit. The installation steps are listed below.

9.2. Installing CUDA Using Conda

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

conda install cuda -c nvidia

9.3. Uninstalling CUDA Using Conda

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

conda remove cuda

9.4. Installing Previous CUDA Releases

All Conda packages released under a specific CUDA version are labeled with that release version. To install a previous version, include that label in the install command such as:

conda install cuda -c nvidia/label/cuda-11.3.0

9.5. Upgrading from cudatoolkit Package

If you had previously installed CUDA using the cudatoolkit package and want to maintain a similar install footprint, you can limit your installation to the following packages:

  • cuda-libraries-dev

  • cuda-nvcc

  • cuda-nvtx

  • cuda-cupti

Note

Some extra files, such as headers, will be included in this installation which were not included in the cudatoolkit package. If you need to reduce your installation further, replace cuda-libraries-dev with the specific libraries you need.

10. Pip Wheels

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.org/simple

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-nvjitlink-cu12

  • nvidia-cuda-opencl-cu12

These metapackages install the following packages:

  • nvidia-nvml-dev-cu123

  • nvidia-cuda-nvcc-cu123

  • nvidia-cuda-runtime-cu123

  • nvidia-cuda-cupti-cu123

  • nvidia-cublas-cu123

  • nvidia-cuda-sanitizer-api-cu123

  • nvidia-nvtx-cu123

  • nvidia-cuda-nvrtc-cu123

  • nvidia-npp-cu123

  • nvidia-cusparse-cu123

  • nvidia-cusolver-cu123

  • nvidia-curand-cu123

  • nvidia-cufft-cu123

  • nvidia-nvjpeg-cu123

  • nvidia-nvjitlink-cu123

  • nvidia-cuda-opencl-cu123

11. Tarball and Zip Archive Deliverables

In an effort to meet the needs of a growing customer base requiring alternative installer packaging formats, as well as a means of input into community CI/CD systems, tarball and zip archives are available for each component.

These tarball and zip archives are provided at https://developer.download.nvidia.com/compute/cuda/redist/.

_images/tarball-archives.png

These .tar.xz and .zip archives do not replace existing packages such as .deb, .rpm, runfile, conda, etc. and are not meant for general consumption, as they are not installers. However this standardized approach will replace existing .txz archives.

For each release, a JSON manifest is provided such as redistrib_11.4.2.json, which corresponds to the CUDA 11.4.2 release label (CUDA 11.4 update 2) which includes the release date, the name of each component, license name, relative URL for each platform and checksums.

Package maintainers are advised to check the provided LICENSE for each component prior to redistribution. Instructions for developers using CMake and Bazel build systems are provided in the next sections.

11.1. Parsing Redistrib JSON

The following example of a JSON manifest contains keys for each component: name, license, version, and a platform array which includes relative_path, sha256, md5, and size (bytes) for each archive.

{
    "release_date": "2021-09-07",
    "cuda_cudart": {
        "name": "CUDA Runtime (cudart)",
        "license": "CUDA Toolkit",
        "version": "11.4.108",
        "linux-x86_64": {
            "relative_path": "cuda_cudart/linux-x86_64/cuda_cudart-linux-x86_64-11.4.108-archive.tar.xz",
            "sha256": "d08a1b731e5175aa3ae06a6d1c6b3059dd9ea13836d947018ea5e3ec2ca3d62b",
            "md5": "da198656b27a3559004c3b7f20e5d074",
            "size": "828300"
        },
        "linux-ppc64le": {
            "relative_path": "cuda_cudart/linux-ppc64le/cuda_cudart-linux-ppc64le-11.4.108-archive.tar.xz",
            "sha256": "831dffe062ae3ebda3d3c4010d0ee4e40a01fd5e6358098a87bb318ea7c79e0c",
            "md5": "ca73328e3f8e2bb5b1f2184c98c3a510",
            "size": "776840"
        },
        "linux-sbsa": {
            "relative_path": "cuda_cudart/linux-sbsa/cuda_cudart-linux-sbsa-11.4.108-archive.tar.xz",
            "sha256": "2ab9599bbaebdcf59add73d1f1a352ae619f8cb5ccec254093c98efd4c14553c",
            "md5": "aeb5c19661f06b6398741015ba368102",
            "size": "782372"
        },
        "windows-x86_64": {
            "relative_path": "cuda_cudart/windows-x86_64/cuda_cudart-windows-x86_64-11.4.108-archive.zip",
            "sha256": "b59756c27658d1ea87a17c06d064d1336576431cd64da5d1790d909e455d06d3",
            "md5": "7f6837a46b78198402429a3760ab28fc",
            "size": "2897751"
        }
    }
}

A JSON schema is provided at https://developer.download.nvidia.com/compute/redist/redistrib-v2.schema.json.

A sample script that parses these JSON manifests is available on GitHub:

  • Downloads each archive

  • Validates SHA256 checksums

  • Extracts archives

  • Flattens into a collapsed directory structure

Table 7 Available Tarball and Zip Archives

Product

Example

CUDA Toolkit

./parse_redist.py --product cuda --label 12.3.2

cuBLASMp

./parse_redist.py --product cublasmp --label 0.1.0

cuDNN

./parse_redist.py --product cudnn --label 8.9.6.50

cuDSS

./parse_redist.py --product cudss --label 0.1.0

cuQuantum

./parse_redist.py --product cuquantum --label 23.10.0

cuSPARSELt

./parse_redist.py --product cusparselt --label 0.5.2

cuTENSOR

./parse_redist.py --product cutensor --label 1.7.0

NVIDIA driver

./parse_redist.py --product nvidia-driver --label 535.129.03

nvJPEG2000

./parse_redist.py --product nvjpeg2000 --label 0.7.5

NVPL

./parse_redist.py --product nvpl --label 23.11

nvTIFF

./parse_redist.py --product nvtiff --label 0.3.0

11.2. Importing Tarballs into CMake

The recommended module for importing these tarballs into the CMake build system is via FindCUDAToolkit (3.17 and newer).

Note

The FindCUDA module is deprecated.

The path to the extraction location can be specified with the CUDAToolkit_ROOT environmental variable. For example CMakeLists.txt and commands, see cmake/1_FindCUDAToolkit/.

For older versions of CMake, the ExternalProject_Add module is an alternative method. For example CMakeLists.txt file and commands, see cmake/2_ExternalProject/.

11.3. Importing Tarballs into Bazel

The recommended method of importing these tarballs into the Bazel build system is using http_archive and pkg_tar.

For an example, see bazel/1_pkg_tar/.

12. CUDA Cross-Platform Environment

Cross development for arm64-sbsa is supported on Ubuntu 20.04, Ubuntu 22.04, RHEL 8, RHEL 9, and SLES 15.

Cross development for arm64-Jetson is only supported on Ubuntu 20.04

We recommend selecting a host development environment that matches the supported cross-target environment. This selection helps prevent possible host/target incompatibilities, such as GCC or GLIBC version mismatches.

12.1. CUDA Cross-Platform Installation

Some of the following steps may have already been performed as part of the native Ubuntu installation. Such steps can safely be skipped.

These steps should be performed on the x86_64 host system, rather than the target system. To install the native CUDA Toolkit on the target system, refer to the native Ubuntu installation section.

  1. Perform the pre-installation actions.

  2. Install repository meta-data package with:

    sudo dpkg -i cuda-repo-cross-<identifier>_all.deb
    

    where <identifier> indicates the operating system, architecture, and/or the version of the package.

  3. Update the Apt repository cache:

    sudo apt-get update
    
  4. Install the appropriate cross-platform CUDA Toolkit:

    1. For aarch64:

      sudo apt-get install cuda-cross-aarch64
      
    2. For QNX:

      sudo apt-get install cuda-cross-qnx
      
  5. Perform the post-installation actions.

12.2. CUDA Cross-Platform Samples

CUDA Samples are now located in https://github.com/nvidia/cuda-samples, which includes instructions for obtaining, building, and running the samples.

13. Post-installation Actions

The post-installation actions must be manually performed. These actions are split into mandatory, recommended, and optional sections.

13.1. Mandatory Actions

Some actions must be taken after the installation before the CUDA Toolkit and Driver can be used.

13.1.1. Environment Setup

The PATH variable needs to include export PATH=/usr/local/cuda-12./bin${PATH:+:${PATH}}. Nsight Compute has moved to /opt/nvidia/nsight-compute/ only in rpm/deb installation method. When using .run installer it is still located under /usr/local/cuda-12.2/.

To add this path to the PATH variable:

export PATH=/usr/local/cuda-12.2/bin${PATH:+:${PATH}}

In addition, when using the runfile installation method, the LD_LIBRARY_PATH variable needs to contain /usr/local/cuda-12.2/lib64 on a 64-bit system, or /usr/local/cuda-12.2/lib on a 32-bit system

  • To change the environment variables for 64-bit operating systems:

    export LD_LIBRARY_PATH=/usr/local/cuda-12.2/lib64\
                             ${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
    
  • To change the environment variables for 32-bit operating systems:

    export LD_LIBRARY_PATH=/usr/local/cuda-12.2/lib\
                             ${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
    

Note that the above paths change when using a custom install path with the runfile installation method.

13.1.2. POWER9 Setup

Because of the addition of new features specific to the NVIDIA POWER9 CUDA driver, there are some additional setup requirements in order for the driver to function properly. These additional steps are not handled by the installation of CUDA packages, and failure to ensure these extra requirements are met will result in a non-functional CUDA driver installation.

There are two changes that need to be made manually after installing the NVIDIA CUDA driver to ensure proper operation:

  1. The NVIDIA Persistence Daemon should be automatically started for POWER9 installations. Check that it is running with the following command:

    systemctl status nvidia-persistenced
    

    If it is not active, run the following command:

    sudo systemctl enable nvidia-persistenced
    
  2. Disable a udev rule installed by default in some Linux distributions that cause hot-pluggable memory to be automatically onlined when it is physically probed. This behavior prevents NVIDIA software from bringing NVIDIA device memory online with non-default settings. This udev rule must be disabled in order for the NVIDIA CUDA driver to function properly on POWER9 systems.

    On RedHat Enterprise Linux 8.1, this rule can be found in:

    /lib/udev/rules.d/40-redhat.rules
    

    On Ubuntu 18.04, this rule can be found in:

    /lib/udev/rules.d/40-vm-hotadd.rules
    

    The rule generally takes a form where it detects the addition of a memory block and changes the ‘state’ attribute to online. For example, in RHEL8, the rule looks like this:

    SUBSYSTEM=="memory", ACTION=="add", PROGRAM="/bin/uname -p", RESULT!="s390*", ATTR{state}=="offline", ATTR{state}="online"
    

    This rule must be disabled by copying the file to /etc/udev/rules.d and commenting out, removing, or changing the hot-pluggable memory rule in the /etc copy so that it does not apply to POWER9 NVIDIA systems. For example, on RHEL 7.5 and earlier:

    sudo cp /lib/udev/rules.d/40-redhat.rules /etc/udev/rules.d
    sudo sed -i '/SUBSYSTEM=="memory", ACTION=="add"/d' /etc/udev/rules.d/40-redhat.rules
    

    On RHEL 7.6 and later versions:

    sudo cp /lib/udev/rules.d/40-redhat.rules /etc/udev/rules.d
    sudo sed -i 's/SUBSYSTEM!="memory",.*GOTO="memory_hotplug_end"/SUBSYSTEM=="*", GOTO="memory_hotplug_end"/' /etc/udev/rules.d/40-redhat.rules
    

You will need to reboot the system to initialize the above changes.

Note

For NUMA best practices on IBM Newell POWER9, see NUMA Best Practices.

13.3. Optional Actions

Other options are not necessary to use the CUDA Toolkit, but are available to provide additional features.

13.3.1. Install Third-party Libraries

Some CUDA samples use third-party libraries which may not be installed by default on your system. These samples attempt to detect any required libraries when building.

If a library is not detected, it waives itself and warns you which library is missing. To build and run these samples, you must install the missing libraries. In cases where these dependencies are not installed, follow the instructions below.

RHEL 7 / CentOS 7

sudo yum install freeglut-devel libX11-devel libXi-devel libXmu-devel \
    make mesa-libGLU-devel freeimage-devel libglfw3-devel

RHEL 8 / Rocky Linux 8

sudo dnf install freeglut-devel libX11-devel libXi-devel libXmu-devel \
    make mesa-libGLU-devel freeimage-devel libglfw3-devel

RHEL 9 / Rocky Linux 9

sudo dnf install freeglut-devel libX11-devel libXi-devel libXmu-devel \
                    make mesa-libGLU-devel freeimage-devel libglfw3-devel

KylinOS 10

sudo dnf install freeglut-devel libX11-devel libXi-devel libXmu-devel \
                    make mesa-libGLU-devel freeimage-devel libglfw3-devel

Fedora

sudo dnf install freeglut-devel libX11-devel libXi-devel libXmu-devel \
    make mesa-libGLU-devel freeimage-devel libglfw3-devel

SLES

sudo zypper install libglut3 libX11-devel libXi6 libXmu6 libGLU1 make

OpenSUSE

sudo zypper install freeglut-devel libX11-devel libXi-devel libXmu-devel \
    make Mesa-libGL-devel freeimage-devel

Ubuntu

sudo apt-get install g++ freeglut3-dev build-essential libx11-dev \
    libxmu-dev libxi-dev libglu1-mesa-dev libfreeimage-dev libglfw3-dev

Debian

sudo apt-get install g++ freeglut3-dev build-essential libx11-dev \
    libxmu-dev libxi-dev libglu1-mesa-dev libfreeimage-dev libglfw3-dev

13.3.2. Install the Source Code for cuda-gdb

The cuda-gdb source must be explicitly selected for installation with the runfile installation method. During the installation, in the component selection page, expand the component “CUDA Tools 12.2” and select cuda-gdb-src for installation. It is unchecked by default.

To obtain a copy of the source code for cuda-gdb using the RPM and Debian installation methods, the cuda-gdb-src package must be installed.

The source code is installed as a tarball in the /usr/local/cuda-12.2/extras directory.

13.3.3. Select the Active Version of CUDA

For applications that rely on the symlinks /usr/local/cuda and /usr/local/cuda-MAJOR, you may wish to change to a different installed version of CUDA using the provided alternatives.

To show the active version of CUDA and all available versions:

update-alternatives --display cuda

To show the active minor version of a given major CUDA release:

update-alternatives --display cuda-12

To update the active version of CUDA:

sudo update-alternatives --config cuda

14. Advanced Setup

Below is information on some advanced setup scenarios which are not covered in the basic instructions above.

Table 8 Advanced Setup Scenarios when Installing CUDA

Scenario

Instructions

Install CUDA using the Package Manager installation method without installing the NVIDIA GL libraries.

Fedora

Install CUDA using the following command:

sudo dnf install cuda-toolkit-12-0 \
    nvidia-driver-cuda akmod-nvidia

Follow the instructions here to ensure that Nouveau is disabled.

If performing an upgrade over a previous installation, the NVIDIA kernel module may need to be rebuilt by following the instructions here.

OpenSUSE/SLES

On some system configurations the NVIDIA GL libraries may need to be locked before installation using:

sudo zypper addlock nvidia-glG04

Install CUDA using the following command:

sudo zypper install --no-recommends cuda-toolkit-12-0 \
    nvidia-computeG04 \
    nvidia-gfxG04-kmp-default

Follow the instructions here to ensure that Nouveau is disabled.

Ubuntu

This functionality isn’t supported on Ubuntu. Instead, the driver packages integrate with the Bumblebee framework to provide a solution for users who wish to control what applications the NVIDIA drivers are used for. See Ubuntu’s Bumblebee wiki for more information.

Upgrade from a RPM/Deb driver installation which includes the diagnostic driver packages to a

driver installation which does not include the diagnostic driver packages.

RHEL/CentOS

Remove diagnostic packages using the following command:

sudo yum remove cuda-drivers-diagnostic \
    xorg-x11-drv-nvidia-diagnostic

Follow the instructions here to continue installation as normal.

Fedora

Remove diagnostic packages using the following command:

sudo dnf remove cuda-drivers-diagnostic \
    xorg-x11-drv-nvidia-diagnostic

Follow the instructions here to continue installation as normal.

OpenSUSE/SLES

Remove diagnostic packages using the following command:

sudo zypper remove cuda-drivers-diagnostic \
    nvidia-diagnosticG04

Follow the instructions here to continue installation as normal.

Ubuntu

Remove diagnostic packages using the following command:

sudo apt-get purge cuda-drivers-diagnostic \
    nvidia-384-diagnostic

Follow the instructions here to continue installation as normal.

Use a specific GPU for rendering the display.

Add or replace a Device entry in your xorg.conf file, located at /etc/X11/xorg.conf. The Device entry should resemble the following:

Section "Device"
    Identifier    "Device0"
    Driver        "driver_name"
    VendorName    "vendor_name"
    BusID         "bus_id"
EndSection

The details will you will need to add differ on a case-by-case basis. For example, if you have two NVIDIA GPUs and you want the first GPU to be used for display, you would replace “driver_name” with “nvidia”, “vendor_name” with “NVIDIA Corporation” and “bus_id” with the Bus ID of the GPU. The Bus ID will resemble “PCI:00:02.0” and can be found by running lspci.

Install CUDA to a specific directory using the Package Manager installation method.

RPM

The RPM packages don’t support custom install locations through the package managers (Yum and Zypper), but it is possible to install the RPM packages to a custom location using rpm’s --relocate parameter:

sudo rpm --install --relocate /usr/local/cuda-12.0=/new/toolkit package.rpm

You will need to install the packages in the correct dependency order; this task is normally taken care of by the package managers. For example, if package “foo” has a dependency on package “bar”, you should install package “bar” first, and package “foo” second. You can check the dependencies of a RPM package as follows:

rpm -qRp package.rpm

Note that the driver packages cannot be relocated.

Deb

The Deb packages do not support custom install locations. It is however possible to extract the contents of the Deb packages and move the files to the desired install location. See the next scenario for more details one xtracting Deb packages.

Extract the contents of the installers.

Runfile

The Runfile can be extracted into the standalone Toolkit and Driver Runfiles by using the --extract parameter. The Toolkit standalone Runfiles can be further extracted by running:

./runfile.run --tar mxvf

The Driver Runfile can be extracted by running:

./runfile.run -x

RPM

The RPM packages can be extracted by running:

rpm2cpio package.rpm | cpio -idmv

Deb

The Deb packages can be extracted by running:

dpkg-deb -x package.deb output_dir

Modify Ubuntu’s apt package manager to query specific architectures for specific repositories.

This is useful when a foreign architecture has been added, causing “404 Not Found” errors to appear when the repository meta-data is updated.

Each repository you wish to restrict to specific architectures must have its sources.list entry modified. This is done by modifying the /etc/apt/sources.list file and any files containing repositories you wish to restrict under the /etc/apt/sources.list.d/ directory. Normally, it is sufficient to modify only the entries in /etc/apt/sources.list

An architecture-restricted repository entry looks like:

deb [arch=<arch1>,<arch2>] <url>

For example, if you wanted to restrict a repository to only the amd64 and i386 architectures, it would look like:

deb [arch=amd64,i386] <url>

It is not necessary to restrict the deb-src repositories, as these repositories don’t provide architecture-specific packages.

For more details, see the sources.list manpage.

The nvidia.ko kernel module fails to load, saying some symbols are unknown.

For example:

nvidia: Unknown symbol drm_open (err 0)

Check to see if there are any optionally installable modules that might provide these symbols which are not currently installed.

For the example of the drm_open symbol, check to see if there are any packages which provide drm_open and are not already installed. For instance, on Ubuntu 14.04, the linux-image-extra package provides the DRM kernel module (which provides drm_open). This package is optional even though the kernel headers reflect the availability of DRM regardless of whether this package is installed or not.

The runfile installer fails to extract due to limited space in the TMP directory.

This can occur on systems with limited storage in the TMP directory (usually /tmp), or on systems which use a tmpfs in memory to handle temporary storage. In this case, the --tmpdir command-line option should be used to instruct the runfile to use a directory with sufficient space to extract into. More information on this option can be found here.

Re-enable Wayland after installing the RPM driver on Fedora.

Wayland is disabled during installation of the Fedora driver RPM due to compatability issues. To re-enable Wayland, comment out this line in /etc/gdm/custom.conf:

WaylandEnable=false

In case of the error: E: Failed to fetch file:/var/cuda-repo File not found

Debian and Ubuntu

This can occur when installing CUDA after uninstalling a different version. Use the following command before installation:

sudo rm -v /var/lib/apt/lists/*cuda* /var/lib/apt/lists/*nvidia*

Verbose installation on Debian and Ubuntu

Use the --verbose-versions flag, for example:

sudo apt-get install --verbose-versions cuda

15. Frequently Asked Questions

15.1. How do I install the Toolkit in a different location?

The Runfile installation asks where you wish to install the Toolkit during an interactive install. If installing using a non-interactive install, you can use the --toolkitpath parameter to change the install location:

./runfile.run --silent \
                --toolkit --toolkitpath=/my/new/toolkit

The RPM and Deb packages cannot be installed to a custom install location directly using the package managers. See the “Install CUDA to a specific directory using the Package Manager installation method” scenario in the Advanced Setup section for more information.

15.2. Why do I see “nvcc: No such file or directory” when I try to build a CUDA application?

Your PATH environment variable is not set up correctly. Ensure that your PATH includes the bin directory where you installed the Toolkit, usually /usr/local/cuda-12.0/bin.

export PATH=/usr/local/cuda-12.0/bin${PATH:+:${PATH}}

15.3. Why do I see “error while loading shared libraries: <lib name>: cannot open shared object file: No such file or directory” when I try to run a CUDA application that uses a CUDA library?

Your LD_LIBRARY_PATH environment variable is not set up correctly. Ensure that your LD_LIBRARY_PATH includes the lib and/or lib64 directory where you installed the Toolkit, usually /usr/local/cuda-12.0/lib{,64}:

export LD_LIBRARY_PATH=/usr/local/cuda-12.0/lib\
                         ${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

15.4. Why do I see multiple “404 Not Found” errors when updating my repository meta-data on Ubuntu?

These errors occur after adding a foreign architecture because apt is attempting to query for each architecture within each repository listed in the system’s sources.list file. Repositories that do not host packages for the newly added architecture will present this error. While noisy, the error itself does no harm. Please see the Advanced Setup section for details on how to modify your sources.list file to prevent these errors.

15.5. How can I tell X to ignore a GPU for compute-only use?

To make sure X doesn’t use a certain GPU for display, you need to specify which other GPU to use for display. For more information, please refer to the “Use a specific GPU for rendering the display” scenario in the Advanced Setup section.

15.6. Why doesn’t the cuda-repo package install the CUDA Toolkit and Drivers?

When using RPM or Deb, the downloaded package is a repository package. Such a package only informs the package manager where to find the actual installation packages, but will not install them.

See the Package Manager Installation section for more details.

15.7. How do I get CUDA to work on a laptop with an iGPU and a dGPU running Ubuntu14.04?

After installing CUDA, set the driver value for the intel device in /etc/X11/xorg.conf to ‘modesetting’ as shown below:

Section "Device"
    Identifier "intel"
    Driver "modesetting"
    ...
EndSection

To prevent Ubuntu from reverting the change in xorg.conf, edit /etc/default/grub to add “nogpumanager” to GRUB_CMDLINE_LINUX_DEFAULT.

Run the following command to update grub before rebooting:

sudo update-grub

15.8. What do I do if the display does not load, or CUDA does not work, after performing a system update?

System updates may include an updated Linux kernel. In many cases, a new Linux kernel will be installed without properly updating the required Linux kernel headers and development packages. To ensure the CUDA driver continues to work when performing a system update, rerun the commands in the Kernel Headers and Development Packages section.

Additionally, on Fedora, the Akmods framework will sometimes fail to correctly rebuild the NVIDIA kernel module packages when a new Linux kernel is installed. When this happens, it is usually sufficient to invoke Akmods manually and regenerate the module mapping files by running the following commands in a virtual console, and then rebooting:

sudo akmods --force
sudo depmod

You can reach a virtual console by hitting ctrl+alt+f2 at the same time.

15.9. How do I install a CUDA driver with a version less than 367 using a network repo?

To install a CUDA driver at a version earlier than 367 using a network repo, the required packages will need to be explicitly installed at the desired version. For example, to install 352.99, instead of installing the cuda-drivers metapackage at version 352.99, you will need to install all required packages of cuda-drivers at version 352.99.

15.10. How do I install an older CUDA version using a network repo?

Depending on your system configuration, you may not be able to install old versions of CUDA using the cuda metapackage. In order to install a specific version of CUDA, you may need to specify all of the packages that would normally be installed by the cuda metapackage at the version you want to install.

If you are using yum to install certain packages at an older version, the dependencies may not resolve as expected. In this case you may need to pass “--setopt=obsoletes=0” to yum to allow an install of packages which are obsoleted at a later version than you are trying to install.

15.11. Why does the installation on SUSE install the Mesa-dri-nouveau dependency?

This dependency comes from the SUSE repositories and shouldn’t affect the use of the NVIDIA driver or the CUDA Toolkit. To disable this dependency, you can lock that package with the following command:

sudo zypper al Mesa-dri-nouveau

15.12. How do I handle “Errors were encountered while processing: glx-diversions”?

This sometimes occurs when trying to uninstall CUDA after a clean .deb installation. Run the following commands:

sudo apt-get install glx-diversions --reinstall
sudo apt-get remove nvidia-alternative

Then re-run the commands from Removing CUDA Toolkit and Driver.

16. Additional Considerations

Now that you have CUDA-capable hardware and the NVIDIA CUDA Toolkit installed, you can examine and enjoy the numerous included programs. To begin using CUDA to accelerate the performance of your own applications, consult the CUDA C++ Programming Guide, located in /usr/local/cuda-12.0/doc.

A number of helpful development tools are included in the CUDA Toolkit to assist you as you develop your CUDA programs, such as NVIDIA® Nsight™ Eclipse Edition, NVIDIA Visual Profiler, CUDA-GDB, and CUDA-MEMCHECK.

For technical support on programming questions, consult and participate in the developer forums at https://forums.developer.nvidia.com/c/accelerated-computing/cuda/206.

17. Switching between Driver Module Flavors

Use the following steps to switch between the NVIDIA driver legacy and open module flavors on your system.

Note

If switching to open module, experimental support for GeForce and Quadro SKUs can be enabled with:

echo "options nvidia NVreg_OpenRmEnableUnsupportedGpus=1" | sudo tee /etc/modprobe.d/nvidia-gsp.conf

Note

Replace XXX with the NVIDIA driver branch number such as 515 or 520.

Fedora, RHEL 9 / Rocky Linux 9, RHEL 8 / Rocky Linux 8

To switch from legacy to open:

sudo dnf module switch-to nvidia-driver:XXX-open --allowerasing

To switch from open to legacy:

sudo dnf module switch-to nvidia-driver:XXX-dkms --allowerasing

Kylin OS

To switch between legacy and open: uninstall, then reinstall.

Ubuntu

To switch from legacy to open:

sudo apt-get --purge remove nvidia-kernel-source-XXX
sudo apt-get install --verbose-versions nvidia-kernel-open-XXX
sudo apt-get install --verbose-versions cuda-drivers-XXX

To switch from open to legacy:

sudo apt-get remove --purge nvidia-kernel-open-XXX
sudo apt-get install --verbose-versions cuda-drivers-XXX

Debian

To switch from legacy to open:

sudo apt-get --purge remove nvidia-kernel-dkms
sudo apt-get install --verbose-versions nvidia-kernel-open-dkms
sudo apt-get install --verbose-versions cuda-drivers-XXX

To switch from open to legacy:

sudo apt-get remove --purge nvidia-kernel-open-dkms
sudo apt-get install --verbose-versions cuda-drivers-XXX

OpenSUSE

To switch from legacy to open:

sudo zypper remove nvidia-gfxG05-kmp-default
sudo zypper install --details nvidia-open-gfxG05-kmp-default
sudo zypper install --details cuda-drivers-XXX

To switch from open to legacy:

sudo zypper remove nvidia-gfxG05-open-kmp-default
sudo zypper install --details cuda-drivers-XXX

SLES

To switch from legacy to open:

sudo zypper remove nvidia-gfxG05-kmp-default nvidia-gfxG05-kmp-azure
sudo zypper install --details nvidia-open-gfxG05-kmp-default nvidia-open-gfxG05-kmp-azure
sudo zypper install --details cuda-drivers-XXX

To switch from open to legacy:

sudo zypper remove nvidia-gfxG05-open-kmp-default nvidia-gfxG05-open-kmp-azure
sudo zypper install --details cuda-drivers-XXX

Note

The Azure package is only available for SLES (x86_64).

RHEL 7 / CentOS 7

To switch from legacy to open:

version=DRIVER_VERSION;
kernel_stream="open-dkms";
stream="latest-dkms";
list=("kmod-nvidia-$kernel_stream-$version")
list+=("nvidia-driver-$stream-cuda-$version")
list+=("nvidia-driver-$stream-cuda-libs-$version")
list+=("nvidia-driver-$stream-devel-$version")
list+=("nvidia-driver-$stream-$version")
list+=("nvidia-driver-$stream-NVML-$version")
list+=("nvidia-driver-$stream-NvFBCOpenGL-$version")
list+=("nvidia-driver-$stream-libs-$version")
list+=("nvidia-libXNVCtrl-$version")
list+=("nvidia-libXNVCtrl-devel-$version")
list+=("nvidia-modprobe-$stream-$version")
list+=("nvidia-persistenced-$stream-$version")
list+=("nvidia-settings-$version")
list+=("nvidia-xconfig-$stream-$version")
sudo yum swap kmod-nvidia-latest-dkms kmod-nvidia-open-dkms-$version
sudo yum --setopt=obsoletes=0  install ${list[@]}
sudo yum install cuda-drivers-DRIVER_BRANCH

To switch from open to legacy:

version=DRIVER_VERSION;
kernel_stream="latest-dkms";
stream="latest-dkms";
list=("kmod-nvidia-$kernel_stream-$version")
list+=("nvidia-driver-$stream-cuda-$version")
list+=("nvidia-driver-$stream-cuda-libs-$version")
list+=("nvidia-driver-$stream-devel-$version")
list+=("nvidia-driver-$stream-$version")
list+=("nvidia-driver-$stream-NVML-$version")
list+=("nvidia-driver-$stream-NvFBCOpenGL-$version")
list+=("nvidia-driver-$stream-libs-$version")
list+=("nvidia-libXNVCtrl-$version")
list+=("nvidia-libXNVCtrl-devel-$version")
list+=("nvidia-modprobe-$stream-$version")
list+=("nvidia-persistenced-$stream-$version")
list+=("nvidia-settings-$version")
list+=("nvidia-xconfig-$stream-$version")
sudo yum swap kmod-nvidia-open-dkms kmod-nvidia-latest-dkms-$version
sudo yum --setopt=obsoletes=0  install ${list[@]}
sudo yum install cuda-drivers-DRIVER_BRANCH

18. Removing CUDA Toolkit and Driver

Follow the below steps to properly uninstall the CUDA Toolkit and NVIDIA Drivers from your system. These steps will ensure that the uninstallation will be clean.

KylinOS 10

To remove CUDA Toolkit:

sudo dnf remove "cuda*" "*cublas*" "*cufft*" "*cufile*" "*curand*" \
                    "*cusolver*" "*cusparse*" "*gds-tools*" "*npp*" "*nvjpeg*" "nsight*" "*nvvm*"

To remove NVIDIA Drivers:

sudo dnf module remove --all nvidia-driver

To reset the module stream:

sudo dnf module reset nvidia-driver

RHEL 9 / Rocky Linux 9

To remove CUDA Toolkit:

sudo dnf remove "cuda*" "*cublas*" "*cufft*" "*cufile*" "*curand*" \
 "*cusolver*" "*cusparse*" "*gds-tools*" "*npp*" "*nvjpeg*" "nsight*" "*nvvm*"

To remove NVIDIA Drivers:

sudo dnf module remove --all nvidia-driver

To reset the module stream:

sudo dnf module reset nvidia-driver

RHEL 8 / Rocky Linux 8

To remove CUDA Toolkit:

sudo dnf remove "cuda*" "*cublas*" "*cufft*" "*cufile*" "*curand*" \
 "*cusolver*" "*cusparse*" "*gds-tools*" "*npp*" "*nvjpeg*" "nsight*" "*nvvm*"

To remove NVIDIA Drivers:

sudo dnf module remove --all nvidia-driver

To reset the module stream:

sudo dnf module reset nvidia-driver

RHEL 7 / CentOS 7

To remove CUDA Toolkit:

sudo yum remove "cuda*" "*cublas*" "*cufft*" "*cufile*" "*curand*" \
 "*cusolver*" "*cusparse*" "*gds-tools*" "*npp*" "*nvjpeg*" "nsight*" "*nvvm*"

To remove NVIDIA Drivers:

sudo yum remove "*nvidia*"

Fedora

To remove CUDA Toolkit:

sudo dnf remove "cuda*" "*cublas*" "*cufft*" "*cufile*" "*curand*" \
 "*cusolver*" "*cusparse*" "*gds-tools*" "*npp*" "*nvjpeg*" "nsight*" "*nvvm*"

To remove NVIDIA Drivers:

sudo dnf module remove --all nvidia-driver

To reset the module stream:

sudo dnf module reset nvidia-driver

To remove 3rd party NVIDIA Drivers:

sudo dnf remove "*nvidia*"

OpenSUSE / SLES

To remove CUDA Toolkit:

sudo zypper remove "cuda*" "*cublas*" "*cufft*" "*cufile*" "*curand*" \
 "*cusolver*" "*cusparse*" "*gds-tools*" "*npp*" "*nvjpeg*" "nsight*" "*nvvm*"

To remove NVIDIA Drivers:

sudo zypper remove "*nvidia*"

Ubuntu and Debian

To remove CUDA Toolkit:

sudo apt-get --purge remove "*cuda*" "*cublas*" "*cufft*" "*cufile*" "*curand*" \
 "*cusolver*" "*cusparse*" "*gds-tools*" "*npp*" "*nvjpeg*" "nsight*" "*nvvm*"

To remove NVIDIA Drivers:

sudo apt-get --purge remove "*nvidia*" "libxnvctrl*"

To clean up the uninstall:

sudo apt-get autoremove

19. Notices

19.1. Notice

This document is provided for information purposes only and shall not be regarded as a warranty of a certain functionality, condition, or quality of a product. NVIDIA Corporation (“NVIDIA”) makes no representations or warranties, expressed or implied, as to the accuracy or completeness of the information contained in this document and assumes no responsibility for any errors contained herein. NVIDIA shall have no liability for the consequences or use of such information or for any infringement of patents or other rights of third parties that may result from its use. This document is not a commitment to develop, release, or deliver any Material (defined below), code, or functionality.

NVIDIA reserves the right to make corrections, modifications, enhancements, improvements, and any other changes to this document, at any time without notice.

Customer should obtain the latest relevant information before placing orders and should verify that such information is current and complete.

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19.2. OpenCL

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

19.3. Trademarks

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