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.

Note

Instructions for installing NVIDIA Drivers are now in the Driver installation guide.

1.1. System Requirements

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

CUDA-capable GPU
A supported version of Linux with a gcc compiler and toolchain
CUDA Toolkit (available at https://developer.nvidia.com/cuda-downloads)

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.

The columns with $distro, $arch, and $arch_ext can be used to replace the occurrences of the same variables across this document.

Table 1 Native Linux Distribution Support in CUDA 12.9
Distribution	$distro	$arch	$arch_ext
x86_64
Red Hat Enterprise Linux 9	rhel9	x86_64	x86_64
Red Hat Enterprise Linux 8	rhel8	x86_64	x86_64
OpenSUSE Leap 15 SP6	opensuse15	x86_64	x86_64
Rocky Linux 9	rhel9	x86_64	x86_64
Rocky Linux 8	rhel8	x86_64	x86_64
SUSE Linux Enterprise Server 15 SP6	sles15	x86_64	x86_64
Ubuntu 24.04 LTS	ubuntu2404	x86_64	amd64
Ubuntu 22.04 LTS	ubuntu2204	x86_64	amd64
Ubuntu 20.04 LTS	ubuntu2004	x86_64	amd64
Debian 12	debian12	x86_64	amd64
Fedora 41	fedora41	x86_64	x86_64
KylinOS V10 SP3 2403	kylin10	x86_64	x86_64
Azure Linux 2.0 (CBL Mariner 2.0)	cm2	x86_64	x86_64
Azure Linux 3.0	azl3	x86_64	x86_64
Amazon Linux 2023	amzn2023	x86_64	x86_64
Oracle Linux 9	rhel9	x86_64	x86_64
Oracle Linux 8	rhel8	x86_64	x86_64
Ubuntu 24.04 (WSL)	wsl-ubuntu	x86_64	x86_64
arm64-sbsa (generic)
Red Hat Enterprise Linux 9	rhel9	sbsa	aarch64
Red Hat Enterprise Linux 8	rhel8	sbsa	aarch64
SUSE Linux Enterprise Server 15 SP6	sles15	sbsa	aarch64
Kylin V10 SP3 2403	kylin10	sbsa	aarch64
Ubuntu 24.04 LTS	ubuntu2404	sbsa	arm64
Ubuntu 22.04 LTS	ubuntu2204	sbsa	arm64
Ubuntu 20.04 LTS	ubuntu2004	sbsa	arm64
arm64-sbsa (GRACE only)
Azure Linux 3.0	azl3	sbsa	aarch64
Amazon Linux 2023	amzn2023	sbsa	aarch64
Red Hat Enterprise Linux 9	rhel9	sbsa	aarch64
SUSE Linux Enterprise Server 15 SP6	sles15	sbsa	aarch64
Ubuntu 24.04 LTS	ubuntu2404	sbsa	arm64
Ubuntu 22.04 LTS	ubuntu2204	sbsa	arm64
arm64-jetson (dGPU)
Ubuntu 22.04 LTS - JetPack 6.x	ubuntu2204	aarch64	arm64
Ubuntu 20.04 LTS - JetPack 5.x	ubuntu2004	aarch64	arm64
arm64-jetson (iGPU)
Ubuntu 22.04 LTS - JetPack 6.x	ubuntu2204	aarch64	arm64

For specific kernel versions supported on Red Hat Enterprise Linux (RHEL), visit https://access.redhat.com/articles/3078.

A list of kernel versions including the release dates for SUSE Linux Enterprise Server (SLES) is available at https://www.suse.com/support/kb/doc/?id=000019587.

1.2. OS Support Policy

Support for the different operating systems will be until the standard EOSS/EOL date as defined for each operating system. Please refer to the support lifecycle for these operating systems to know their support timelines and plan to move to newer releases accordingly.

CUDA supports the latest Fedora release version. The version supported might require a specific GCC compatibility package. 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 - 14.x	7.x - 19.x	24.9	No	No	2021.7
Arm64 sbsa	6.x - 14.x	7.x - 19.x	24.9	No	24.04	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. Host Compiler Compatibility Packages

Really up to date distributions might ship with a newer compiler than what is covered by the Supported Compilers table above. Usually, those distribution also provide a GCC compatibility package that can be used instead of the default one.

Depending on the distribution, the package that needs to be installed is different, but the logic for configuring it is the same. If required, configuration steps are described in the relevant section for the specific Linux distribution, but they always end up with configuring the NVCC_BIN environment variable as described in the NVCC documentation.

1.3.2. 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.

GCC	Clang	NVHPC	Arm C/C++
>=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.

1.4.1. Administrative Privileges

Commands which can be executed as a normal user will be prefixed by a $ at the beginning of the line
Commands which require administrative privilege (root) will be prefixed by a # at the beginning of the line

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 or a sudo shell (sudo -i) for all the necessary commands.

2. Pre-installation Actions

Some actions must be taken before the CUDA Toolkit 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.
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:

$ hostnamectl

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. 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.

2.5. 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 tools needed to create, build and run a CUDA application as well as libraries, header files, and other resources.

Download Verification

If you are using the local stand alone or run file installer, the download can be verified by comparing the MD5 checksum posted at https://developer.download.nvidia.com/compute/cuda/12.9.0/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>

2.6. 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
Installing Toolkit Version X.Y	run	Uninstall RPM/deb	Uninstall Run	No Action	No Action

Use the following command to uninstall a Toolkit runfile installation:

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

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

Red Hat Enterprise Linux, Rocky Linux, Oracle Linux, Fedora, KylinOS, Amazon Linux:

# dnf remove <package_name>

Azure Linux:

# tdnf remove <package_name>

OpenSUSE Leap, SUSE Linux Enterprise Server:

# zypper remove <package_name>

Debian / Ubuntu:

# apt --purge remove <package_name>

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, tdnf, or zypper.

Distribution-specific instructions detail how to install CUDA:

Red Hat Enterprise Linux / Rocky Linux / Oracle Linux
KylinOS
Fedora
SUSE Linux Enterprise Server
OpenSUSE Leap
Windows Subsystem for Linux
Ubuntu
Debian
Amazon Linux
Azure Linux

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. Red Hat Enterprise Linux / Rocky Linux / Oracle Linux

3.2.1. Preparation

Perform the Pre-installation Actions.

Satisfy third-party package dependencies by enabling optional repositories:

Red Hat Enterprise Linux 9:

# subscription-manager repos --enable=rhel-9-for-$arch-appstream-rpms
# subscription-manager repos --enable=rhel-9-for-$arch-baseos-rpms
# subscription-manager repos --enable=codeready-builder-for-rhel-9-$arch-rpms

Red Hat Enterprise Linux 8:

# subscription-manager repos --enable=rhel-8-for-$arch-appstream-rpms
# subscription-manager repos --enable=rhel-8-for-$arch-baseos-rpms
# subscription-manager repos --enable=codeready-builder-for-rhel-8-$arch-rpms

Rocky Linux 9:
```
# dnf config-manager --set-enabled crb
```

Rocky Linux 8:

# dnf config-manager --set-enabled powertools

Oracle Linux 9:

# dnf config-manager --set-enabled ol9_codeready_builder

Oracle Linux 8:

# dnf config-manager --set-enabled ol8_codeready_builder

Choose an installation method: Local Repository Installation or Network Repository Installation.

3.2.2. Local Repository Installation

Install local repository on file system:

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

3.2.3. Network Repository Installation

Enable the network repository:

# dnf config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/<distro>/<arch>/cuda-<distro>.repo

3.2.4. Common Instructions

These instructions apply to both local and network installations.

Install CUDA SDK:
```
# dnf install cuda-toolkit
```
Install GPUDirect Filesystem:
```
# dnf install nvidia-gds
```
Reboot the system:
```
# reboot
```
Perform the post-installation actions.

3.3. KylinOS

3.3.1. Preparation

Perform the pre-installation actions.
Choose an installation method: Local Repository Installation or Network Repository Installation.

3.3.2. Local Repository Installation

Install local repository on file system:

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

3.3.3. Network Repository Installation

Enable the network repository:

# dnf config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/<distro>/<arch>/cuda-<distro>.repo

3.3.4. Common Instructions

These instructions apply to both local and network installation.

Install CUDA SDK:
```
# dnf install cuda-toolkit
```
Install GPUDirect Filesystem:
```
# dnf install nvidia-gds
```
Reboot the system:
```
# reboot
```
Perform the post-installation actions.

3.4. Fedora

3.4.1. Preparation

Perform the pre-installation actions.
Choose an installation method: Local Repository Installation or Network Repository Installation.

3.4.2. Local Repository Installation

Install local repository on file system:

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

3.4.3. Network Repository Installation

Enable the network repository:

# dnf config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/<distro>/x86_64/cuda-<distro>.repo

3.4.4. Common Installation Instructions

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

Install CUDA SDK:
```
# dnf install cuda-toolkit
```
Reboot the system:
```
# reboot
```
Perform the Post-installation Actions.

3.4.5. GCC Compatibility Package for Fedora

The Fedora version supported might ship with a newer compiler than what is actually supported by NVCC. This can be overcome by installing the GCC compatibility package and setting a few environment variables.

As an example, Fedora 41 ships with GCC 14 and also with a compatible GCC 13 version, which can be used for NVCC. To install and configure the local NVCC binary to use that version, proceed as follows.

Install the packages required:
```
# dnf install gcc13-c++
```
The binaries then appear on the system in the following way:
```
/usr/bin/gcc-13
/usr/bin/g++-13
```
Override the default g++ compiler. Refer to the documentation for NVCC regarding the environment variables. For example:
```
$ export NVCC_CCBIN='g++-13'
```

3.5. SUSE Linux Enterprise Server

3.5.1. Preparation

Perform the Pre-installation Actions.
Choose an installation method: Local Repository Installation or Network Repository Installation.

3.5.2. Local Repository Installation

Install local repository on file system:

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

3.5.3. Network Repository Installation

Enable the network repository:

# zypper addrepo https://developer.download.nvidia.com/compute/cuda/repos/<distro>/<arch>/cuda-<distro>.repo

Refresh Zypper repository cache:

# SUSEConnect --product PackageHub/<SLES version number>/<arch>
# zypper refresh

3.5.4. Common Installation Instructions

These instructions apply to both local and network installation for SUSE Linux Enterprise Server.

Install CUDA SDK:
```
# zypper install cuda-toolkit
```
Reboot the system:
```
# reboot
```
Perform the Post-installation Actions.

3.6. OpenSUSE Leap

3.6.1. Preparation

Perform the Pre-installation Actions.
Choose an installation method: Local Repository Installation or Network Repository Installation.

3.6.2. Local Repository Installation

Install local repository on file system:

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

3.6.3. Network Repository Installation

Enable the network repository:

# zypper addrepo https://developer.download.nvidia.com/compute/cuda/repos/<distro>/x86_64/cuda-<distro>.repo

Refresh Zypper repository cache:
```
# zypper refresh
```

3.6.4. Common Installation Instructions

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

Install CUDA SDK:
```
# zypper install cuda-toolkit
```
Reboot the system:
```
# reboot
```
Perform the Post-installation Actions.

3.7. Windows Subsystem for Linux

These instructions must be used if you are installing in a WSL environment.

3.7.1. Preparation

Perform the Pre-installation Actions.
Choose an installation method: Local Repository Installation or Network Repository Installation.

3.7.2. Local Repository Installation

Install local repository on file system:

# dpkg -i cuda-repo-<distro>-X-Y-local_<version>*_amd64.deb

Enroll ephemeral public GPG key:

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

Add pin file to prioritize CUDA repository:

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

3.7.3. Network Repository Installation

Install the cuda-keyring package:

$ wget https://developer.download.nvidia.com/compute/cuda/repos/<distro>/x86_64/cuda-keyring_1.1-1_all.deb
# dpkg -i cuda-keyring_1.1-1_all.deb

3.7.4. Common Installation Instructions

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

Update the Apt repository cache:
```
# apt update
```
Install CUDA SDK:
```
# apt install cuda-toolkit
```
Perform the Post-installation Actions.

3.8. Ubuntu

3.8.1. Prepare Ubuntu

Perform the Pre-installation Actions.
Choose an installation method: Local Repository Installation or Network Repository Installation.

3.8.2. Local Repository Installation

Install local repository on file system:

# dpkg -i cuda-repo-<distro>-X-Y-local_<version>*_<arch>.deb

Enroll ephemeral public GPG key:

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

Add pin file to prioritize CUDA repository:

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

3.8.3. Network Repository Installation

Install the 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

# dpkg -i cuda-keyring_1.1-1_all.deb

3.8.4. Common Installation Instructions

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

Update the APT repository cache:
```
# apt update
```
Install CUDA SDK:

Note

These two commands must be executed separately.
```
# apt install cuda-toolkit
```
To include all GDS packages:
```
# apt install nvidia-gds
```
For native arm64-jetson repositories, install the additional packages:
```
# apt install cuda-compat
```
Reboot the system:
```
# reboot
```
Perform the Post-installation Actions.

3.9. Debian

3.9.1. Preparation

Perform the Pre-installation Actions.
Enable the contrib repository:
```
# add-apt-repository contrib
```
Choose an installation method: Local Repository Installation or Network Repository Installation.

3.9.2. Local Repository Installation

Install local repository on file system:

# dpkg -i cuda-repo-<distro>-X-Y-local_<version>*_amd64.deb

Enroll public GPG key:

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

3.9.3. Network Repository Installation

Install the cuda-keyring package:

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

3.9.4. Common Installation Instructions

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

Update the APT repository cache:
```
# apt update
```
Install CUDA SDK:
```
# apt install cuda-toolkit
```
Reboot the system:
```
# reboot
```
Perform the Post-installation Actions.

3.10. Amazon Linux

3.10.1. Prepare Amazon Linux

Perform the Pre-installation Actions.
Choose an installation method: Local Repository Installation or Network Repository Installation.

3.10.2. Local Repository Installation

Install local repository on file system:

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

3.10.3. Network Repository Installation

Enable the network repository:

# dnf config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/<distro>/x86_64/cuda-<distro>.repo

3.10.4. Common Installation Instructions

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

Install CUDA SDK:
```
# dnf install cuda-toolkit
```
Install GPUDirect Filesystem:
```
# dnf install nvidia-gds
```
Reboot the system:
```
# reboot
```
Perform the post-installation actions.

3.11. Azure Linux

3.11.1. Prepare Azure Linux

Perform the Pre-installation Actions.
Choose an installation method: Local Repository Installation or Network Repository Installation.

3.11.2. Local Repository Installation

Install local repository on file system:

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

3.11.3. Network Repository Installation

Enable the network repository:

# curl https://developer.download.nvidia.com/compute/cuda/repos/<distro>/x86_64/cuda-<distro>.repo -o /etc/yum.repos.d/cuda-<distro>.repo

3.11.4. Common Installation Instructions

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

Enable the extended repository:

Azure Linux 2 (CBL Mariner 2.0):

# tdnf install mariner-repos-extended

Azure Linux 3:

# tdnf install azurelinux-repos-extended

Install Cuda SDK:
```
# tdnf install cuda-toolkit
```
Install GPUDirect Filesystem:
```
# tdnf install nvidia-gds
```
Reboot the system:
```
# reboot
```
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-toolkit package. This package will install the full set of other CUDA packages required for native development and should cover most scenarios. This 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.

On supported platforms, the cuda-cross-aarch64 and cuda-cross-sbsa packages install all the packages required for cross-platform development to arm64-jetson and SBSA, respectively.

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. Hopper does not support 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:

Amazon Linux / Fedora / KylinOS / Red Hat Enterprise Linux / Rocky Linux / Oracle Linux:

# dnf --disablerepo="*" --enablerepo="cuda*" list

Azure Linux:

# tdnf --disablerepo="*" --enablerepo="cuda-cm2-<cuda X-Y version>-local" list

SUSE Linux Enterprise Server / openSUSE Leap:

# zypper packages -r cuda

Debian / Ubuntu:

# cat /var/lib/apt/lists/*cuda*Packages | grep "Package:"

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 4 Meta Packages Available for CUDA 12.9
Meta Package	Purpose
cuda	Installs all CUDA Toolkit and driver packages with a full desktop experience. Installs also the next version of the `cuda` package when it’s released.
cuda-12-9	Installs all CUDA Toolkit and driver packages at the version specified until an additional version of CUDA is installed.
cuda-toolkit	Installs all CUDA Toolkit packages with a full desktop experience. Installs also the next version of the `cuda-toolkit` package when it’s released.
cuda-toolkit-12	Installs all CUDA Toolkit packages with a full desktop experience. Will not upgrade beyond the 12.x series toolkits.
cuda-toolkit-12	Installs all CUDA Toolkit packages with a full desktop experienc at the version specified until an additional version of CUDA is installed.
cuda-tools-12-9	Installs all CUDA command line and visual tools. Will not upgrade beyond the 12.x series toolkits.
cuda-runtime-12-9	Installs all CUDA Toolkit packages required to run CUDA applications and driver, without any desktop component. Specific for compute nodes
cuda-compiler-12-9	Installs all CUDA compiler packages.
cuda-libraries-12-9	Installs all runtime CUDA Library packages.
cuda-libraries-dev-12-9	Installs all development CUDA Library packages.

3.12.3. 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:

3.12.3.1. Amazon Linux

# dnf upgrade cuda-toolkit

3.12.3.2. Fedora

When upgrading the toolkit to a new major branch:

# dnf install cuda-toolkit

When upgrading the toolkit to a new minor branch:

# dnf upgrade cuda-toolkit

3.12.3.3. KylinOS / Red Hat Enterprise Linux / Rocky Linux / Oracle Linux

# dnf install cuda-toolkit

3.12.3.4. Azure Linux

# tdnf install cuda-toolkit

3.12.3.5. OpenSUSE / SUSE Linux Enterprise Server

# zypper install cuda-toolkit

3.12.3.6. Debian / Ubuntu

# apt install cuda-toolkit

3.12.3.7. Other Package Notes

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

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

Side-by-side installations are supported. As described in the Meta Packages section, depending on the package you can avoid the upgrades or get the new version installed automatically.

4. Driver Installation

More information about driver installation can be found in the Driver Installation Guide for Linux

5. 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.

5.1. Runfile Overview

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

The installation steps are listed below.

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.

5.2. Installation

Perform the pre-installation actions.
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.
Run the installer and follow the on-screen prompts:
```
# sh cuda_<version>_linux.run
```
The installer will prompt for the following:
- EULA Acceptance
- CUDA Toolkit installation, location, and /usr/local/cuda symbolic link
The default installation location for the toolkit is /usr/local/cuda-12.9:

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 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.
Reboot the system to reload the graphical interface:
```
# reboot
```
Perform the post-installation actions.

5.3. 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-12.9` 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.).
Kernel Module Build Directory	`--kernel-module-build-directory=<kernel\|kernel-open>`	Tells the driver installation to use legacy or open flavor of kernel source when building the NVIDIA kernel module. The kernel-open flavor is only supported on Turing GPUs and newer.
	`-m=kernel`	Tells the driver installation to use legacy flavor of kernel source when building the NVIDIA kernel module. Shorthand for `--kernel-module-build-directory=kernel`
	`m=kernel-open`	Tells the driver installation to use open flavor of kernel source when building the NVIDIA kernel module. The kernel-open flavor is only supported on Turing GPUs and newer. Shorthand for `--kernel-module-build-directory=kernel-open`
Show Installer Options	`--help`	Prints the list of command-line options to stdout.

5.4. 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-12.9/bin:

# /usr/local/cuda-12.9/bin/cuda-uninstaller

6. 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.

6.1. Conda Overview

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

6.2. 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

Note

Install CUDA in a dedicated Conda environment instead of the base environment to avoid installation issues.

6.3. Uninstalling CUDA Using Conda

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

$ conda remove cuda

6.4. 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-12.4.0

6.5. 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.

7. 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.

7.1. 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

7.2. 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>

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

These metapackages install the following packages:

nvidia-cublas-cu129
nvidia-cuda-cccl-cu129
nvidia-cuda-cupti-cu129
nvidia-cuda-nvcc-cu129
nvidia-cuda-nvrtc-cu129
nvidia-cuda-opencl-cu129
nvidia-cuda-runtime-cu129
nvidia-cuda-sanitizer-api-cu129
nvidia-cufft-cu129
nvidia-curand-cu129
nvidia-cusolver-cu129
nvidia-cusparse-cu129
nvidia-npp-cu129
nvidia-nvfatbin-cu129
nvidia-nvjitlink-cu129
nvidia-nvjpeg-cu129
nvidia-nvml-dev-cu129
nvidia-nvtx-cu129

8. CUDA Cross-Platform Environment

Cross development for arm64-sbsa is supported on Ubuntu 20.04, Ubuntu 22.04, Ubuntu 24.04, KylinOS 10, Red Hat Enterprise Linux 8, Red Hat Enterprise Linux 9, and SUSE Linux Enterprise Server 15.

Cross development for arm64-jetson is only supported on Ubuntu 22.04.

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.

8.1. CUDA Cross-Platform Installation

Some of the following steps may have already been performed as part of the native installation sections. 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 installation sections in Package Manager Installation.

8.1.1. Ubuntu

Perform the Pre-installation Actions.
Choose an installation method: Local Cross Repository Installation or Network Cross Repository Installation.

8.1.1.1. Local Cross Repository Installation

Install repository meta-data package with:

# dpkg -i cuda-repo-cross-<arch>-<distro>-X-Y-local-<version>*_all.deb

8.1.1.2. Network Cross Repository Installation

Install the cuda-keyring package:

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

8.1.1.3. Common Installation Instructions

Update the APT repository cache:

# apt update

Install the appropriate cross-platform CUDA Toolkit:

For arm64-sbsa:
```
# apt install cuda-cross-sbsa
```
For arm64-jetson:
```
# apt install cuda-cross-aarch64
```
For QNX:
```
# apt install cuda-cross-qnx
```

Perform the Post-installation Actions.

8.1.2. Red Hat Enterprise Linux / Rocky Linux / Oracle Linux

Perform the Pre-installation Actions
Choose an installation method: Local Cross Repository Installation or Network Cross Repository Installation.

8.1.2.1. Local Cross Repository Installation

Install repository meta-data package with:

# rpm -i cuda-repo-cross-<arch>-<distro>-X-Y-local-<version>*.noarch.rpm

8.1.2.2. Network Cross Repository Installation

Enable the network repository:

# dnf config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/<distro>/cross-linux-<arch>/cuda-<distro>-cross-linux-sbsa.repo

8.1.2.3. Common Installation Instructions

Install the CUDA SDK:
```
# dnf install cuda-cross-sbsa
```

8.1.3. SUSE Linux Enterprise Server

Perform the Pre-installation Actions
Choose an installation method: Local Cross Repository Installation or Network Cross Repository Installation.

8.1.3.1. Local Cross Repository Installation

Install repository meta-data package with:

# rpm -i cuda-repo-cross-<arch>-<distro>-X-Y-local-<version>*.noarch.rpm

8.1.3.2. Network Cross Repository Installation

Enable the network repo:

# zypper addrepo https://developer.download.nvidia.com/compute/cuda/repos/<distro>/<arch>/cuda-<distro>-cross-linux-sbsa.repo

8.1.3.3. Common Installation Instructions

Refresh Zypper repository cache:
```
# zypper refresh
```
Install CUDA SDK:
```
# zypper install cuda-cross-sbsa
```

9. 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, known as binary archives, are provided at https://developer.download.nvidia.com/compute/cuda/redist/.

These component .tar.xz and .zip binary 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.

9.1. 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 5 Available Tarball and Zip Archives
Product	Example
CUDA Toolkit	`./parse_redist.py --product cuda --label 12.9.0`
cuBLASMp	`./parse_redist.py --product cublasmp --label 0.2.1`
cuDNN	`./parse_redist.py --product cudnn --label 9.2.1`
cuDSS	`./parse_redist.py --product cudss --label 0.3.0`
cuQuantum	`./parse_redist.py --product cuquantum --label 24.03.0`
cuSPARSELt	`./parse_redist.py --product cusparselt --label 0.6.2`
cuTENSOR	`./parse_redist.py --product cutensor --label 2.0.2.1`
NVIDIA driver	`./parse_redist.py --product nvidia-driver --label 550.90.07`
nvJPEG2000	`./parse_redist.py --product nvjpeg2000 --label 0.7.5`
NVPL	`./parse_redist.py --product nvpl --label 24.7`
nvTIFF	`./parse_redist.py --product nvtiff --label 0.3.0`

9.2. 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/.

9.3. 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/.

10. Post-installation Actions

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

10.1. Mandatory Actions

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

10.1.1. Environment Setup

The PATH variable needs to include export PATH=/usr/local/cuda-12.9/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-12.9/.

To add this path to the PATH variable:

$ export PATH=${PATH}:/usr/local/cuda-12.9/bin

In addition, when using the runfile installation method, the LD_LIBRARY_PATH variable needs to contain /usr/local/cuda-12.9/lib64 on a 64-bit system and /usr/local/cuda-12.9/lib for the 32 bit compatibility:

$ export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda-12.9/lib64

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

10.2. Recommended Actions

Other actions are recommended to verify the integrity of the installation.

10.2.1. Install Writable Samples

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

10.2.2. Verify the Installation

Before continuing, it is important to verify that the CUDA toolkit can find and communicate correctly with the CUDA-capable hardware. To do this, you need to compile and run some of the sample programs, located in https://github.com/nvidia/cuda-samples.

Note

Ensure the PATH and, if using the runfile installation method, LD_LIBRARY_PATH variables are set correctly.

10.2.2.1. Running the Binaries

After compilation, find and run deviceQueryfrom https://github.com/nvidia/cuda-samples. If the CUDA software is installed and configured correctly, the output for deviceQuery should look similar to that shown in Figure 1.

Figure 1 Valid Results from deviceQuery CUDA Sample

The exact appearance and the output lines might be different on your system. The important outcomes are that a device was found (the first highlighted line), that the device matches the one on your system (the second highlighted line), and that the test passed (the final highlighted line).

If a CUDA-capable device is installed but deviceQuery reports that no CUDA-capable devices are present, this likely means that the /dev/nvidia* files are missing or have the wrong permissions.

On systems where SELinux is enabled, you might need to temporarily disable this security feature to run deviceQuery. To do this, type:

setenforce 0

from the command line as the superuser.

Running the bandwidthTest program ensures that the system and the CUDA-capable device are able to communicate correctly. Its output is shown in Figure 2.

Figure 2 Valid Results from bandwidthTest CUDA Sample

Note that the measurements for your CUDA-capable device description will vary from system to system. The important point is that you obtain measurements, and that the second-to-last line (in Figure 2) confirms that all necessary tests passed.

Should the tests not pass, make sure you have a CUDA-capable NVIDIA GPU on your system and make sure it is properly installed.

If you run into difficulties with the link step (such as libraries not being found), consult the Linux Release Notes found in https://github.com/nvidia/cuda-samples.

10.2.3. Install Nsight Eclipse Plugins

To install Nsight Eclipse plugins, an installation script is provided:

$ /usr/local/cuda-12.9/bin/nsight_ee_plugins_manage.sh install <eclipse-dir>

Refer to Nsight Eclipse Plugins Installation Guide for more details.

10.2.4. Local Repo Removal

Removal of the local repo installer is recommended after installation of CUDA SDK.

Debian / Ubuntu

# apt-get remove --purge "cuda-repo-<distro>-X-Y-local*"

Amazon Linux / Fedora / KylinOS / RHEL / Rocky Linux / Oracle Linux

# dnf remove "cuda-repo-<distro>-X-Y-local*"

Azure Linux

# tdnf remove "cuda-repo-<distro>-X-Y-local*"

OpenSUSE / SLES

# zypper remove "cuda-repo-<distro>-X-Y-local*"

10.3. Optional Actions

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

10.3.1. 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.

Amazon Linux / Fedora / KylinOS / RHEL / Rocky Linux / Oracle Linux

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

SLES

# zypper install libglut3 libX11-devel libXi6 libXmu6 libGLU1 make

OpenSUSE

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

Debian / Ubuntu

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

10.3.2. 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 12.8” 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-12.8/extras directory.

10.3.3. 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:

# update-alternatives --config cuda

11. Removing CUDA Toolkit

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

Amazon Linux / Fedora / Kylin OS / Red Hat Enterprise Linux / Rocky Linux / Oracle Linux:

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

Azure Linux:

# tdnf remove "cuda*" "*cublas*" "*cufft*" "*cufile*" "*curand*" "*cusolver*" "*cusparse*" "*gds-tools*" "*npp*" "*nvjpeg*" "nsight*" "*nvvm*"

And then to clean up the uninstall:

# tdnf autoremove

OpenSUSE / SUSE Linux Enterprise Server:

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

Debian / Ubuntu:

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

And then to clean up the uninstall:

# apt autoremove --purge

12. Advanced Setup

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

Table 6 Advanced Setup Scenarios when Installing CUDA
Scenario	Instructions
Install GPUDirect Storage	Refer to Installing GPUDirect Storage. GDS is supported in two different modes: GDS (default/full perf mode) 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, Ubuntu 24.04 RHEL 8.y (y <= 10), RHEL 9.y (y <= 5)
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-12.9=/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 Runfiles by using the `--extract` parameter. The Toolkit standalone Runfiles can be further extracted by running: ./runfile.run --tar mxvf ./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 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 in Advanced Options.
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

13. 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-12.8/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.

14. Frequently Asked Questions

14.1. 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.

14.2. 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-12.8/bin.

$ export PATH=/usr/local/cuda-12.9/bin${PATH:+:${PATH}}

14.3. 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-12.8/lib{,64}:

$ export LD_LIBRARY_PATH=/usr/local/cuda-12.9/lib ${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

14.4. 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.

14.5. 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.

14.6. Why doesn’t the cuda-repo package install the CUDA Toolkit?

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.

14.7. 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.

14.8. 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:

# apt install glx-diversions --reinstall
# apt remove nvidia-alternative

Then re-run the commands from Removing CUDA Toolkit.

15. Notices

15.1. 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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NVIDIA makes no representation or warranty that products based on this document will be suitable for any specified use. Testing of all parameters of each product is not necessarily performed by NVIDIA. It is customer’s sole responsibility to evaluate and determine the applicability of any information contained in this document, ensure the product is suitable and fit for the application planned by customer, and perform the necessary testing for the application in order to avoid a default of the application or the product. Weaknesses in customer’s product designs may affect the quality and reliability of the NVIDIA product and may result in additional or different conditions and/or requirements beyond those contained in this document. NVIDIA accepts no liability related to any default, damage, costs, or problem which may be based on or attributable to: (i) the use of the NVIDIA product in any manner that is contrary to this document or (ii) customer product designs.

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15.2. OpenCL

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

15.3. Trademarks

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

16. Copyright

This product includes software developed by the Syncro Soft SRL (http://www.sync.ro/).