Installation Guide for Linux
Release 12.6
NVIDIA Corporation
Aug 1, 2024
Contents
1 System Requirements 3
2 OS Support Policy 5
3 Host Compiler Support Policy 7
3.1 Supported C++ Dialects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
4 About This Document 9
5 Pre-installation Actions 11
5.1 Verify You Have a CUDA-Capable GPU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
5.2 Verify You Have a Supported Version of Linux . . . . . . . . . . . . . . . . . . . . . . . . . . 12
5.3 Verify the System Has gcc Installed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
5.4 Verify the System has the Correct Kernel Headers and Development Packages Installed 12
5.5 Install GPUDirect Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
5.6 Choose an Installation Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
5.7 Download the NVIDIA CUDA Toolkit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
5.8 Address Custom xorg.conf, If Applicable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
5.9 Handle Conicting Installation Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
6 Package Manager Installation 17
6.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
6.2 RHEL 8 / Rocky 8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
6.2.1 Prepare RHEL 8 / Rocky 8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
6.2.2 Local Repo Installation for RHEL 8 / Rocky 8 . . . . . . . . . . . . . . . . . . . . . . . . . 19
6.2.3 Network Repo Installation for RHEL 8 / Rocky 8 . . . . . . . . . . . . . . . . . . . . . . . 19
6.2.4 Common Instructions for RHEL 8 / Rocky 8 . . . . . . . . . . . . . . . . . . . . . . . . . . 19
6.3 RHEL 9 / Rocky 9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
6.3.1 Prepare RHEL 9 / Rocky 9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
6.3.2 Local Repo Installation for RHEL 9 / Rocky 9 . . . . . . . . . . . . . . . . . . . . . . . . . 21
6.3.3 Network Repo Installation for RHEL 9 / Rocky 9 . . . . . . . . . . . . . . . . . . . . . . . 21
6.3.4 Common Instructions for RHEL 9 / Rocky 9 . . . . . . . . . . . . . . . . . . . . . . . . . . 21
6.4 KylinOS 10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
6.4.1 Prepare KylinOS 10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
6.4.2 Local Repo Installation for KylinOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
6.4.3 Network Repo Installation for KylinOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
6.4.4 Common Instructions for KylinOS 10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
6.5 Fedora . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
6.5.1 Prepare Fedora . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
6.5.2 Local Repo Installation for Fedora . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
6.5.3 Network Repo Installation for Fedora . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
6.5.4 Common Installation Instructions for Fedora . . . . . . . . . . . . . . . . . . . . . . . . . 24
6.6 SLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
6.6.1 Prepare SLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
i
6.6.2 Local Repo Installation for SLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
6.6.3 Network Repo Installation for SLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
6.6.4 Common Installation Instructions for SLES . . . . . . . . . . . . . . . . . . . . . . . . . . 26
6.7 OpenSUSE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
6.7.1 Prepare OpenSUSE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
6.7.2 Local Repo Installation for OpenSUSE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
6.7.3 Network Repo Installation for OpenSUSE . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
6.7.4 Common Installation Instructions for OpenSUSE . . . . . . . . . . . . . . . . . . . . . . 28
6.8 WSL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
6.8.1 Prepare WSL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
6.8.2 Local Repo Installation for WSL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
6.8.3 Network Repo Installation for WSL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
6.8.4 Common Installation Instructions for WSL . . . . . . . . . . . . . . . . . . . . . . . . . . 29
6.9 Ubuntu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
6.9.1 Prepare Ubuntu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
6.9.2 Local Repo Installation for Ubuntu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
6.9.3 Network Repo Installation for Ubuntu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
6.9.4 Common Installation Instructions for Ubuntu . . . . . . . . . . . . . . . . . . . . . . . . 32
6.10 Debian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
6.10.1 Prepare Debian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
6.10.2 Local Repo Installation for Debian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
6.10.3 Network Repo Installation for Debian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
6.10.4 Common Installation Instructions for Debian . . . . . . . . . . . . . . . . . . . . . . . . . 33
6.11 Amazon Linux 2023 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
6.11.1 Prepare Amazon Linux 2023 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
6.11.2 Local Repo Installation for Amazon Linux . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
6.11.3 Network Repo Installation for Amazon Linux . . . . . . . . . . . . . . . . . . . . . . . . . 34
6.11.4 Common Installation Instructions for Amazon Linux . . . . . . . . . . . . . . . . . . . . 35
6.12 Azure Linux CM2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
6.12.1 Prepare Azure Linux CM2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
6.12.2 Local Repo Installation for Azure Linux . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
6.12.3 Network Repo Installation for Azure Linux . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
6.12.4 Common Installation Instructions for Azure Linux . . . . . . . . . . . . . . . . . . . . . . 36
6.13 Additional Package Manager Capabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
6.13.1 Available Packages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
6.13.2 Meta Packages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
6.13.3 Optional 32-bit Packages for Linux x86_64 .deb/.rpm . . . . . . . . . . . . . . . . . . . . 38
6.13.4 Package Upgrades . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
7 Driver Installation 41
8 NVIDIA Proprietary Kernel Modules 45
9 Precompiled Streams 47
9.1 Precompiled Streams Support Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
9.2 Modularity Proles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
10 Kickstart Installation 51
10.1 RHEL 8 / Rocky Linux 8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
10.2 RHEL 9 / Rocky Linux 9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
11 Runle Installation 53
11.1 Runle Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
11.2 Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
11.3 Disabling Nouveau . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
ii
11.3.1 Fedora . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
11.3.2 RHEL / Rocky and KylinOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
11.3.3 OpenSUSE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
11.3.4 SLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
11.3.5 WSL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
11.3.6 Ubuntu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
11.3.7 Debian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
11.4 Device Node Verication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
11.5 Advanced Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
11.6 Uninstallation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
12 Conda Installation 61
12.1 Conda Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
12.2 Installing CUDA Using Conda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
12.3 Uninstalling CUDA Using Conda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
12.4 Installing Previous CUDA Releases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
12.5 Upgrading from cudatoolkit Package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
13 Pip Wheels 63
14 Tarball and Zip Archive Deliverables 65
14.1 Parsing Redistrib JSON . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
14.2 Importing Tarballs into CMake . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
14.3 Importing Tarballs into Bazel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
15 CUDA Cross-Platform Environment 69
15.1 CUDA Cross-Platform Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
15.2 CUDA Cross-Platform Samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
16 Post-installation Actions 71
16.1 Mandatory Actions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
16.1.1 Environment Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
16.2 Recommended Actions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
16.2.1 Install Persistence Daemon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
16.2.2 Install Writable Samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
16.2.3 Verify the Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
16.2.3.1 Verify the Driver Version . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
16.2.3.2 Running the Binaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
16.2.4 Install Nsight Eclipse Plugins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
16.2.5 Local Repo Removal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
16.3 Optional Actions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
16.3.1 Install Third-party Libraries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
16.3.2 Install the Source Code for cuda-gdb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
16.3.3 Select the Active Version of CUDA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
17 Advanced Setup 77
18 Frequently Asked Questions 79
18.1 How do I install the Toolkit in a dierent location? . . . . . . . . . . . . . . . . . . . . . . . . 79
18.2 Why do I see “nvcc: No such le or directory” when I try to build a CUDA application? . 79
18.3 Why do I see “error while loading shared libraries: <lib name>: cannot open shared
object le: No such le or directory” when I try to run a CUDA application that uses a
CUDA library? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
18.4 Why do I see multiple “404 Not Found” errors when updating my repository meta-data
on Ubuntu? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
iii
18.5 How can I tell X to ignore a GPU for compute-only use? . . . . . . . . . . . . . . . . . . . . 80
18.6 Why doesn’t the cuda-repo package install the CUDA Toolkit and Drivers? . . . . . . . . 81
18.7 How do I get CUDA to work on a laptop with an iGPU and a dGPU running Ubuntu14.04? 81
18.8 What do I do if the display does not load, or CUDA does not work, after performing a
system update? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
18.9 How do I install a CUDA driver with a version less than 367 using a network repo? . . . 82
18.10 How do I install an older CUDA version using a network repo? . . . . . . . . . . . . . . . . 82
18.11 Why does the installation on SUSE install the Mesa-dri-nouveau dependency? . . . . . . 82
18.12 How do I handle “Errors were encountered while processing: glx-diversions”? . . . . . . . 83
19 Additional Considerations 85
20 Switching between Driver Module Flavors 87
21 Removing CUDA Toolkit and Driver 89
22 Notices 91
22.1 Notice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
22.2 OpenCL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
22.3 Trademarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
23 Copyright 93
iv
Installation Guide for Linux, Release 12.6
NVIDIA CUDA Installation Guide for Linux
The installation instructions for the CUDA Toolkit on Linux.
CUDA
®
is a parallel computing platform and programming model invented by NVIDIA
®
. It enables dra-
matic 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 fo-
cus on the task of parallelization of the algorithms rather than spending time on their implemen-
tation.
▶ Support heterogeneous computation where applications use both the CPU and GPU. Serial por-
tions of applications are run on the CPU, and parallel portions are ooaded to the GPU. As such,
CUDA can be incrementally applied to existing applications. The CPU and GPU are treated as sep-
arate devices that have their own memory spaces. This conguration 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 le 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.
Contents 1
Installation Guide for Linux, Release 12.6
2 Contents
Chapter 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 environ-
ment, including the host compiler and C runtime libraries, and is therefore only supported on distribu-
tion versions that have been qualied 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 12.6
Distribution Kernel
1
Default GCC GLIBC
x86_64
RHEL 9.y (y <= 4) 5.14.0-427 11.4.1 2.34
RHEL 8.y (y <= 10) 4.18.0-553 8.5.0 2.28
OpenSUSE Leap 15.y (y <= 6) 6.4.0-150600.21 7.5.0 2.38
Rocky Linux 8.y (y<=10) 4.18.0-553 8.5.0 2.28
Rocky Linux 9.y (y<=4) 5.14.0-427 11.4.1 2.34
SUSE SLES 15.y (y <= 6) 6.4.0-150600.21 7.5.0 2.31
Ubuntu 24.04 LTS 6.8.0-31 13.2.0 2.39
Ubuntu 22.04.z (z <= 4) LTS 6.5.0-27 12.3.0 2.35
Ubuntu 20.04.z (z <= 6) LTS 5.15.0-67 9.4.0 2.31
Debian 12.x (x<=6) 6.1.0-22 12.2.0 2.36
Debian 11.y (y<=9)
2
5.10.209-2 10.2.1 2.31
Fedora 39 6.5.6-300 13.2.1 2.38
KylinOS V10 SP3 2403 4.19.90-89.11.v2401 7.3.0 2.28
MSFT Azure Linux 2.0 5.15.158.2-1 11.2.0 2.35
continues on next page
3
Installation Guide for Linux, Release 12.6
Table 1 – continued from previous page
Distribution Kernel
1
Default GCC GLIBC
Amazon Linux 2023 6.1.82-99.168 11.4.1 2.34
Arm64 sbsa
RHEL 9.y (y <= 4) 5.14.0-427 11.4.1 2.34
RHEL 8.y (y <= 10) 4.18.0-553 8.5.0 2.28
SUSE SLES 15.y (y == 6) 6.4.0-150600.21 7.5.0 2.38
MSFT Azure Linux 2.0 5.15.158.2-1 11.2.0 2.35
Kylin V10 SP3 2403 4.19.90-89 12.3.0 2.28
Ubuntu 24.04 LTS 6.8.0-31 13.2.0 2.39
Ubuntu 22.04 LTS (z <= 5) LTS 5.15.0-102 11.4.0 2.35
Ubuntu 20.04.z (z <= 5) LTS 5.4.0-174 9.4.0 2.31
Arm64 sbsa Jetson (dGPU)
20.04.06 LTS Rel35 JP 5.x 5.10.192-tegra 9.4.0 2.31
22.04.4 LTS Rel36 - JP6.x 5.15.136-tegra 11.4.0 2.35
Aarch64 Jetson (iGPU)
L4T Ubuntu 22.04 Rel36 - JP6.x 6.1.80-tegra 11.4.0 2.3.5
(1) The following notes apply to the kernel versions supported by CUDA:
▶ For specic 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.
(2) Support for Debian 11.9 is deprecated.
4 Chapter 1. System Requirements
Chapter 2. OS Support Policy
▶ CUDA support for Ubuntu 20.04.x, Ubuntu 22.04.x, Ubuntu 24.04.x, RHEL 8.x, RHEL 9.x, Rocky
Linux 8.x, Rocky Linux 9.x, SUSE SLES 15.x, OpenSUSE Leap 15.x, Amazon linux 2023, and Azure
Linux 2.0 will be until the standard EOSS as dened for each OS. Please refer to the support
lifecycle for these OSes to know their support timelines.
▶ 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.
5
Installation Guide for Linux, Release 12.6
6 Chapter 2. OS Support Policy
Chapter 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 - 13.2 7.x - 18.0 24.5 No No 2021.7
Arm64 sbsa 6.x - 13.2 7.x - 18.0 24.5 No 24.04 No
For GCC and Clang, the preceding table indicates the minimum version and the latest version sup-
ported. 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.
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 avors of host compiler in both host and device code.
GCC Clang NVHPC Arm C/C++
>=10.x >=11.x >=22.x >=22.x
7
Installation Guide for Linux, Release 12.6
8 Chapter 3. Host Compiler Support Policy
Chapter 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 prex for all necessary commands.
9
Installation Guide for Linux, Release 12.6
10 Chapter 4. About This Document
Chapter 5. 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 conicting installation methods.
Note: You can override the install-time prerequisite checks by running the installer with the
-override ag. Remember that the prerequisites will still be required to use the NVIDIA CUDA Toolkit.
5.1. 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 com-
mand.
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.
11
Installation Guide for Linux, Release 12.6
5.2. Verify You Have a Supported Version of
Linux
The CUDA Development Tools are only supported on some specic 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, modied 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.
5.3. 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.
5.4. 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 Runle 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
12 Chapter 5. Pre-installation Actions
Installation Guide for Linux, Release 12.6
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.
5.5. 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 dierent modes: GDS (default/full perf mode) and Compatibility mode. In-
stallation instructions for them dier 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 <= 4)
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.
5.6. Choose an Installation Method
The CUDA Toolkit can be installed using either of two dierent installation mechanisms: distribution-
specic packages (RPM and Deb packages), or a distribution-independent package (runle packages).
The distribution-independent package has the advantage of working across a wider set of Linux distri-
butions, but does not update the distribution’s native package management system. The distribution-
specic packages interface with the distribution’s native package management system. It is recom-
mended to use the distribution-specic packages, where possible.
5.5. Install GPUDirect Storage 13
Installation Guide for Linux, Release 12.6
Note: For both native as well as cross development, the toolkit must be installed using the
distribution-specic installer. See the CUDA Cross-Platform Installation section for more details.
5.7. 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 applica-
tion as well as libraries, header les, and other resources.
Download Verication
The download can be veried by comparing the MD5 checksum posted at https://developer.download.
nvidia.com/compute/cuda/12.5.1/docs/sidebar/md5sum.txt with that of the downloaded le. If either
of the checksums dier, the downloaded le is corrupt and needs to be downloaded again.
To calculate the MD5 checksum of the downloaded le, run the following:
md5sum <file>
5.8. Address Custom xorg.conf, If Applicable
The driver relies on an automatically generated xorg.conf le at ∕etc∕X11∕xorg.conf. If a custom-
built xorg.conf le is present, this functionality will be disabled and the driver may not work. You
can try removing the existing xorg.conf le, or adding the contents of ∕etc∕X11∕xorg.conf.d∕
00-nvidia.conf to the xorg.conf le. The xorg.conf le will most likely need manual tweaking
for systems with a non-trivial GPU conguration.
5.9. Handle Conicting Installation Methods
Before installing CUDA, any previous installations that could conict should be uninstalled. This will
not aect systems which have not had CUDA installed previously, or systems where the installation
method has been preserved (RPM/Deb vs. Runle). See the following charts for specics.
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
14 Chapter 5. Pre-installation Actions
Installation Guide for Linux, Release 12.6
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 Ver-
sion 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 runle installation:
sudo ∕usr∕local∕cuda-X.Y∕bin∕cuda-uninstaller
Use the following command to uninstall a Driver runle 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 ∕ RHEL 9�
,→∕ Rocky Linux 9 ∕ Fedora ∕ KylinOS 10 ∕ Amazon Linux 2023
sudo tdnf remove <package_name> # Azure Linux
sudo zypper remove <package_name> # OpenSUSE ∕ SLES
sudo apt-get --purge remove <package_name> # Ubuntu
5.9. Handle Conicting Installation Methods 15
Installation Guide for Linux, Release 12.6
16 Chapter 5. Pre-installation Actions
Chapter 6. Package Manager
Installation
Basic instructions can be found in the Quick Start Guide. Read on for more detailed instructions.
6.1. Overview
Installation using RPM or Debian packages interfaces with your system’s package management sys-
tem. When using RPM or Debian local repo installers, the downloaded package contains a repository
snapshot stored on the local lesystem in /var/. Such a package only informs the package manager
where to nd 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-specic instructions detail how to install CUDA:
▶ RHEL 8 / Rocky Linux 8
▶ RHEL 9 / Rocky Linux 9
▶ KylinOS 10
▶ Fedora
▶ SLES
▶ OpenSUSE
▶ WSL
▶ Ubuntu
▶ Debian
▶ Amazon Linux 2023
▶ Azure Linux CM2
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.
17
Installation Guide for Linux, Release 12.6
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.
6.2. RHEL 8 / Rocky 8
6.2.1. 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
4. Remove Outdated Signing Key:
sudo rpm --erase gpg-pubkey-7fa2af80*
5. Choose an installation method: local repo or network repo.
18 Chapter 6. Package Manager Installation
Installation Guide for Linux, Release 12.6
6.2.2. Local Repo Installation for RHEL 8 / Rocky 8
1. Install local repository on le system:
sudo rpm --install cuda-repo-<distro>-X-Y-local-<version>*.<arch>.rpm
6.2.3. 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∕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 rst 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-$distro.repo
3. Clean Yum repository cache:
sudo dnf clean expire-cache
6.2.4. Common Instructions for RHEL 8 / Rocky 8
These instructions apply to both local and network installation.
1. Install CUDA SDK:
sudo dnf module install nvidia-driver:latest-dkms
sudo dnf install cuda-toolkit
2. Install GPUDirect Filesystem:
sudo dnf install nvidia-gds
3. Add libcuda.so symbolic link, if necessary
The libcuda.so library is installed in the ∕usr∕lib{,64}∕nvidia directory. For pre-existing
projects which use libcuda.so, it may be useful to add a symbolic link from libcuda.so in the
∕usr∕lib{,64} directory.
6.2. RHEL 8 / Rocky 8 19
Installation Guide for Linux, Release 12.6
4. Reboot the system:
sudo reboot
5. Perform the post-installation actions.
6.3. RHEL 9 / Rocky 9
6.3.1. Prepare RHEL 9 / Rocky 9
1. Perform the pre-installation actions.
2. The kernel headers and development packages for the currently running kernel can be installed
with:
sudo dnf install kernel-devel-$(uname -r) kernel-headers-$(uname -r)
3. Satisfy third-party package dependency:
▶ Satisfy DKMS dependency: The NVIDIA driver RPM packages depend on other external
packages, such as DKMS and libvdpau. Those packages are only available on third-party
repositories, such as EPEL. Any such third-party repositories must be added to the package
manager repository database before installing the NVIDIA driver RPM packages, or missing
dependencies will prevent the installation from proceeding.
To enable EPEL:
sudo dnf install https:∕∕dl.fedoraproject.org∕pub∕epel∕epel-release-latest-9.
,→noarch.rpm
▶ Enable optional repos:
On RHEL 9 Linux only, execute the following steps to enable optional repositories.
▶ On x86_64 systems:
subscription-manager repos --enable=rhel-9-for-x86_64-appstream-rpms
subscription-manager repos --enable=rhel-9-for-x86_64-baseos-rpms
subscription-manager repos --enable=codeready-builder-for-rhel-9-x86_64-
,→rpms
4. Remove Outdated Signing Key:
sudo rpm --erase gpg-pubkey-7fa2af80*
5. Choose an installation method: local repo or network repo.
20 Chapter 6. Package Manager Installation
Installation Guide for Linux, Release 12.6
6.3.2. Local Repo Installation for RHEL 9 / Rocky 9
1. Install local repository on le system:
sudo rpm --install cuda-repo-<distro>-X-Y-local-<version>*.<arch>.rpm
6.3.3. Network Repo Installation for RHEL 9 / Rocky 9
1. Enable the network repo:
sudo dnf config-manager --add-repo https:∕∕developer.download.nvidia.com∕compute∕
,→cuda∕repos∕$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 rst 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-$distro.repo
3. Clean Yum repository cache:
sudo dnf clean expire-cache
6.3.4. Common Instructions for RHEL 9 / Rocky 9
These instructions apply to both local and network installation.
1. Install CUDA SDK:
sudo dnf module install nvidia-driver:latest-dkms
sudo dnf install cuda-toolkit
2. Install GPUDirect Filesystem:
sudo dnf install nvidia-gds
3. Add libcuda.so symbolic link, if necessary
The libcuda.so library is installed in the ∕usr∕lib{,64}∕nvidia directory. For pre-existing
projects which use libcuda.so, it may be useful to add a symbolic link from libcuda.so in the
∕usr∕lib{,64} directory.
6.3. RHEL 9 / Rocky 9 21
Installation Guide for Linux, Release 12.6
4. Reboot the system:
sudo reboot
5. Perform the post-installation actions.
6.4. KylinOS 10
6.4.1. Prepare KylinOS 10
1. Perform the pre-installation actions.
2. The kernel headers and development packages for the currently running kernel can be installed
with:
sudo dnf install kernel-devel-$(uname -r) kernel-headers-$(uname -r)
3. Choose an installation method: local repo or network repo.
6.4.2. Local Repo Installation for KylinOS
1. Install local repository on le system:
sudo rpm --install cuda-repo-kylin10-X-Y-local-<version>*.<arch>.rpm
6.4.3. Network Repo Installation for KylinOS
1. Enable the network repo:
sudo dnf config-manager --add-repo https:∕∕developer.download.nvidia.com∕compute∕
,→cuda∕repos∕kylin10∕x86_64∕cuda-$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 rst time. Indicate you accept the change when prompted.
3. Clean Yum repository cache:
sudo dnf clean expire-cache
22 Chapter 6. Package Manager Installation
Installation Guide for Linux, Release 12.6
6.4.4. 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.
6.5. Fedora
6.5.1. 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.
6.5.2. Local Repo Installation for Fedora
1. Install local repository on le system:
sudo rpm --install cuda-repo-<distro>-X-Y-local-<version>*.x86_64.rpm
where distro is fedora37 or fedora39, for example.
6.5. Fedora 23
Installation Guide for Linux, Release 12.6
6.5.3. 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 $distro should be replaced by one of the following:
▶ fedora37
▶ fedora39
2. Install the new CUDA public GPG key:
The new GPG public key for the CUDA repository (RPM-based distros) is d42d0685.
On a fresh installation of Fedora, the dnf package manager will prompt the user to accept new
keys when installing packages the rst time. Indicate you accept the change when prompted.
For upgrades, you must also fetch an updated .repo entry:
sudo dnf config-manager --add-repo https:∕∕developer.download.nvidia.com∕compute∕
,→cuda∕repos∕$distro∕x86_64∕cuda-$distro.repo
3. Clean DNF repository cache:
sudo dnf clean expire-cache
6.5.4. Common Installation Instructions 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-toolkit
It may be necessary to rebuild the grub conguration les, 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:
24 Chapter 6. Package Manager Installation
Installation Guide for Linux, Release 12.6
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.
6.6. SLES
6.6.1. 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=$(uname -r | sed 's∕\-default∕∕')
3. The kernel headers and development packages for the currently running kernel can be installed
with:
sudo zypper install -y kernel-<variant>-devel=<version>
4. On SLES12 SP4, install the Mesa-libgl-devel Linux packages before proceeding.
See Mesa-libGL-devel.
5. Add the user to the video group:
sudo usermod -a -G video <username>
6. Remove Outdated Signing Key:
sudo rpm --erase gpg-pubkey-7fa2af80*
7. Choose an installation method: local repo or network repo.
6.6. SLES 25
Installation Guide for Linux, Release 12.6
6.6.2. Local Repo Installation for SLES
1. Install local repository on le system:
sudo rpm --install cuda-repo-sles15-X-Y-local-<version>*.x86_64.rpm
6.6.3. Network Repo Installation for SLES
1. Enable the network repo:
sudo zypper addrepo https:∕∕developer.download.nvidia.com∕compute∕cuda∕repos∕
,→$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 rst 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
6.6.4. 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.
26 Chapter 6. Package Manager Installation
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6.7. OpenSUSE
6.7.1. 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.
6.7.2. Local Repo Installation for OpenSUSE
1. Install local repository on le system:
sudo rpm --install cuda-repo-opensuse15-<version>.x86_64.rpm
6.7.3. 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 rst time. Indicate you accept the change when prompted.
6.7. OpenSUSE 27
Installation Guide for Linux, Release 12.6
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
6.7.4. 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.
6.8. 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.
6.8.1. 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.
28 Chapter 6. Package Manager Installation
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6.8.2. Local Repo Installation for WSL
1. Install local repositiry on le 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∕
6.8.3. 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:
a. 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
b. 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
c. Add pin le 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
6.8.4. 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:
6.8. WSL 29
Installation Guide for Linux, Release 12.6
sudo apt-get install cuda-toolkit
3. Perform the post-installation actions.
6.9. Ubuntu
6.9.1. 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.
6.9.2. Local Repo Installation for Ubuntu
1. Install local repository on le 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 le 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
6.9.3. 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
30 Chapter 6. Package Manager Installation
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where $distro∕$arch should be replaced by one of the following:
▶ ubuntu1604∕x86_64
▶ ubuntu1804∕cross-linux-sbsa
▶ 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: $distro∕cross-linux-aarch64
sudo dpkg -i cuda-keyring_1.1-1_all.deb
Or if you are unable to install the cuda-keyring package, you can optionally:
a. Enroll the new signing key manually:
wget https:∕∕developer.download.nvidia.com∕compute∕cuda∕repos∕<distro>∕<arch>∕
,→cuda-archive-keyring.gpg
sudo mv cuda-archive-keyring.gpg ∕usr∕share∕keyrings∕cuda-archive-keyring.gpg
b. Enable the network repository:
echo "deb [signed-by=∕usr∕share∕keyrings∕cuda-archive-keyring.gpg] https:∕∕
,→developer.download.nvidia.com∕compute∕cuda∕repos∕<distro>∕<arch>∕ ∕" | sudo�
,→tee ∕etc∕apt∕sources.list.d∕cuda-<distro>-<arch>.list
c. Add pin le to prioritize CUDA repository:
wget https:∕∕developer.download.nvidia.com∕compute∕cuda∕repos∕<distro>∕<arch>∕
,→cuda-<distro>.pin
sudo mv cuda-<distro>.pin ∕etc∕apt∕preferences.d∕cuda-repository-pin-600
6.9. Ubuntu 31
Installation Guide for Linux, Release 12.6
6.9.4. Common Installation Instructions for Ubuntu
These instructions apply to both local and network installation for Ubuntu.
1. Update the Apt repository cache:
sudo apt-get update
2. Install CUDA SDK:
Note: These two commands must be executed separately.
sudo apt-get install cuda-toolkit
To include all GDS packages:
sudo apt-get install nvidia-gds
3. Reboot the system
sudo reboot
4. Perform the Post-installation Actions
6.10. Debian
6.10.1. Prepare Debian
1. Perform the pre-installation actions.
2. The kernel headers and development packages for the currently running kernel can be installed
with:
sudo apt-get install linux-headers-$(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.
32 Chapter 6. Package Manager Installation
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6.10.2. Local Repo Installation for Debian
1. Install local repository on le 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∕
6.10.3. 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:
▶ debian11∕x86_64
▶ debian12∕x86_64
sudo dpkg -i cuda-keyring_1.1-1_all.deb
Or if you are unable to install the cuda-keyring package, you can optionally:
a. 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
b. 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
6.10.4. 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 11, you may instead need to run:
6.10. Debian 33
Installation Guide for Linux, Release 12.6
sudo apt-get --allow-releaseinfo-change update
2. Install CUDA SDK:
sudo apt-get -y install cuda-toolkit
3. Reboot the system:
sudo reboot
4. Perform the post-installation actions.
6.11. Amazon Linux 2023
6.11.1. Prepare Amazon Linux 2023
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) kernel-
,→modules-extra-$(uname -r)
3. Choose an installation method: local repo or network repo.
6.11.2. Local Repo Installation for Amazon Linux
1. Install local repository on le system:
sudo rpm --install cuda-repo-amzn2023-X-Y-local-<version>*.x86_64.rpm
6.11.3. Network Repo Installation for Amazon Linux
1. Enable the network repository:
sudo dnf config-manager --add-repo https:∕∕developer.download.nvidia.com∕compute∕
,→cuda∕repos∕amzn2023∕x86_64∕cuda-amzn2023.repo
2. Clean DNF repository cache:
sudo dnf clean expire-cache
34 Chapter 6. Package Manager Installation
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6.11.4. Common Installation Instructions for Amazon
Linux
These instructions apply to both local and network installation for Amazon Linux.
1. Install CUDA SDK:
sudo dnf module install nvidia-driver:latest-dkms
sudo dnf install cuda-toolkit
2. Install GPUDirect Filesystem:
sudo dnf install nvidia-gds
3. Add libcuda.so symbolic link, if necessary:
The libcuda.so library is installed in the ∕usr∕lib{,64}∕nvidia directory. For pre-existing
projects which use libcuda.so, it may be useful to add a symbolic link from libcuda.so in the
∕usr∕lib{,64} directory.
4. Reboot the system:
sudo reboot
5. Perform the post-installation actions.
6.12. Azure Linux CM2
6.12.1. Prepare Azure Linux CM2
1. Perform the pre-installation actions.
The kernel headers and development packages for the currently running kernel can be installed
with:
sudo tdnf install kernel-devel-$(uname -r) kernel-headers-$(uname -r) kernel-
,→modules-extra-$(uname -r)
2. Choose an installation method: local repo or network repo.
6.12.2. Local Repo Installation for Azure Linux
1. Install local repository on le system:
sudo rpm --install cuda-repo-cm2-X-Y-local-<version>*.x86_64.rpm
6.12. Azure Linux CM2 35
Installation Guide for Linux, Release 12.6
6.12.3. Network Repo Installation for Azure Linux
1. Enable the network repository:
sudo tdnf config-manager --add-repo https:∕∕developer.download.nvidia.com∕compute∕
,→cuda∕repos∕cm2∕x86_64∕cuda-cm2.repo
2. Clean TDNF repository cache:
sudo tdnf clean expire-cache
6.12.4. Common Installation Instructions for Azure Linux
These instructions apply to both local and network installation for Azure Linux.
1. Enable Mariner extended repo:
sudo tdnf install mariner-repos-extended
2. Install Cuda SDK:
sudo tdnf install nvidia-open
sudo tdnf install cuda-toolkit
3. Install GPUDirect Filesystem:
sudo tdnf install nvidia-gds
4. Reboot the system:
sudo reboot
5. Perform the post-installation actions.
6.13. Additional Package Manager Capabilities
Below are some additional capabilities of the package manager that users can take advantage of.
6.13.1. 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 com-
piler, the debugger, the proler, the math libraries, and so on. For x86_64 platforms, this also includes
Nsight Eclipse Edition and the visual prolers. It also includes the NVIDIA driver package.
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 arm64-Server, respectively.
36 Chapter 6. Package Manager Installation
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The libraries and header les 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
6.13.2. Meta Packages
Meta packages are RPM/Deb/Conda packages which contain no (or few) les but have multiple de-
pendencies. 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.
6.13. Additional Package Manager Capabilities 37
Installation Guide for Linux, Release 12.6
Table 5: Meta Packages Available for CUDA 12.6
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-12-6 Installs all CUDA Toolkit and Driver packages. Remains at version 12.5 until an
additional version of CUDA is installed.
cuda-toolkit-12-6 Installs all CUDA Toolkit packages required to develop CUDA applications. Does
not include the driver.
cuda-toolkit-16 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-12-6 Installs all CUDA command line and visual tools.
cuda-runtime-12-
6
Installs all CUDA Toolkit packages required to run CUDA applications, as well as
the Driver packages.
cuda-compiler-12-
6
Installs all CUDA compiler packages.
cuda-libraries-12-
6
Installs all runtime CUDA Library packages.
cuda-libraries-
dev-12-6
Installs all development CUDA Library packages.
cuda-drivers Installs all NVIDIA Driver packages with proprietary kernel modules. Handles
upgrading to the next version of the Driver packages when they’re released.
cuda-drivers-560 Installs all NVIDIA Driver packages with proprietary kernel modules. Will not
upgrade beyond the 560.xxx branch drivers.
nvidia-open Installs all NVIDIA Open GPU kernel modules Driver packages. Handles upgrad-
ing to the next version of the Driver packages when they are released.
nvidia-open-560 Installs all NVIDIA Open GPU kernel modules Driver packages. Will not upgrade
beyond the 560.xxx branch drivers.
6.13.3. 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
38 Chapter 6. Package Manager Installation
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For Debian 12:
sudo dpkg --add-architecture i386
sudo apt-get update
sudo apt-get install 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 RHEL:
sudo dnf install nvidia-driver-cuda-libs.i686 \
nvidia-driver-libs.i686 nvidia-driver-NvFBCOpenGL.i686 nvidia-driver-NVML.i686
Note: There is no modularity prole support.
For openSUSE/SLES:
sudo zypper install nvidia-compute-G06-32bit nvidia-gl-G06-32bit nvidia-video-G06-
,→32bit
6.13.4. 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 dnf install cuda-toolkit # Amazon Linux 2023,�
,→Fedora, RHEL8, Rocky Linux 8, RHEL9, Rocky Linux 9, and KylinOS
sudo zypper install cuda-toolkit # OpenSUSE and SLES
sudo apt-get install cuda-toolkit # Ubuntu and Debian
sudo tdnf install cuda-toolkit # Azure Linux
The cuda-cross-<arch> packages can also be upgraded in the same manner.
The nvidia-open package points to the latest open driver release available in the CUDA repository.
When a new version is available, use the following commands to upgrade the driver:
sudo dnf module install nvidia-driver:open-dkms # Amazon Linux 2023,�
,→Fedora, RHEL8, Rocky Linux 8, RHEL9, Rocky Linux 9, and KylinOS
sudo zypper -v install nvidia-open # OpenSUSE and SLES
6.13. Additional Package Manager Capabilities 39
Installation Guide for Linux, Release 12.6
sudo apt-get install -V nvidia-open # Ubuntu and Debian
sudo tdnf install nvidia-open # Azure Linux
Some desktop environments, such as GNOME or KDE, will display a notication alert when new pack-
ages are available.
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. For instance, to install both the X.Y CUDA Toolkit and the
X.Y+1 CUDA Toolkit, install the cuda-toolkit-X.Y and cuda-toolkit-X.Y+1 packages.
40 Chapter 6. Package Manager Installation
Chapter 7. Driver Installation
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 “avors” of these kernel modules are provided:
▶ Proprietary – this is the avor that NVIDIA has historically shipped.
▶ Open-source – published kernel modules that are dual licensed MIT/GPLv2. These are new start-
ing in release 515. With every driver release, the source code to the open kernel modules will be
published on https://github.com/NVIDIA/open-gpu-kernel-modules and a tarball will be provided
on https://download.nvidia.com/XFree86/.
Starting in the 560 driver release series, the open kernel module avor is the default installation.
Verify that your NVIDIA GPU is at least Turing or newer generation.
lspci | grep VGA
Starting in the 545 driver release, it no longer required to manually enable support for GeForce and
Quadro SKUs with:
echo "options nvidia NVreg_OpenRmEnableUnsupportedGpus=1" | sudo tee ∕etc∕modprobe.d∕
,→nvidia-gsp.conf
For simplication, we’ve condensed the package manager recommendations in table format. All re-
leases beyond driver version 560 and CUDA Toolkit 12.6 will use these packaging conventions.
41
Installation Guide for Linux, Release 12.6
Table 6: Package manager installation recommendations
Distro Install the latest Install a specic release
Amazon
Linux/Fedora/RHEL/Rocky
Linux/KylinOS
sudo dnf module install
nvidia-driver:open-dkms
sudo dnf module install
nvidia-driver:560-open
openSUSE/SLES sudo zypper install
nvidia-open{-azure,-64k}
sudo zypper install
nvidia-open-560{-azure,-64k}
Debian sudo apt-get install
nvidia-open
sudo apt-get install
nvidia-open-560
Ubuntu sudo apt-get install
nvidia-open
sudo apt-get install
nvidia-open-560
Azure Linux sudo tdnf install nvidia-open sudo tdnf install
nvidia-open-560
Specify a driver branch to install the highest version in the specied branch.
For Debian and Ubuntu:
sudo apt-get install -V nvidia-open-<driver_branch>
For example:
sudo apt-get install -V nvidia-open-560
For OpenSUSE and SLES:
Note: The default kernel avor is default.
sudo zypper -v install nvidia-open-<driver_branch>
For example:
sudo zypper -v install nvidia-open-560
On SLES 15 (x86_64) the azure kernel avor is available
sudo zypper -v install nvidia-open-azure-<driver_branch>
For example:
sudo zypper -v install nvidia-open-azure-560
On SLES 15 (sbsa) the 64kb kernel avor is available
sudo zypper -v install nvidia-open-64k-<driver_branch>
For example:
sudo zypper -v install nvidia-open-64k-560
For Amazon Linux, Fedora, KylinOS, RHEL, and Rocky Linux:
42 Chapter 7. Driver Installation
Installation Guide for Linux, Release 12.6
sudo dnf module install nvidia-driver:<stream>∕<profile>
where prole by default is default and does not need to be specied.
▶ Example dkms streams: 560-open or open-dkms
To uninstall or change streams on Amazon Linux, Fedora, KylinOS, RHEL, and Rocky Linux:
sudo dnf module remove --all nvidia-driver
sudo dnf module reset nvidia-driver
For Azure Linux:
sudo tdnf install nvidia-open-<driver_branch>
For example:
sudo tdnf install nvidia-open-560
For CUDA Runle:
Pass the CLI argument to the CUDA runle to opt in to NVIDIA Open GPU Kernel Modules:
sh cuda_<release>_<version>_linux.run --kernel-module-type=open
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Installation Guide for Linux, Release 12.6
44 Chapter 7. Driver Installation
Chapter 8. NVIDIA Proprietary Kernel
Modules
For older GPUs from the Maxwell, Pascal, or Volta architectures, the open-source GPU kernel modules
are not compatible with your platform. Continue to use the NVIDIA proprietary driver. Specify a driver
branch to install the highest version in the specied branch.
For Debian and Ubuntu:
sudo apt-get install cuda-drivers-<driver_branch>
For example:
sudo apt-get install cuda-drivers-560
For OpenSUSE and SLES:
sudo zypper -v install cuda-drivers-<driver_branch>
For example:
sudo zypper -v install cuda-drivers-560
For Amazon Linux, Fedora, KylinOS, RHEL, and Rocky Linux:
sudo dnf module install nvidia-driver:<stream>∕<profile>
where prole by default is default and does not need to be specied.
▶ Example dkms streams: 560-dkms or latest-dkms
▶ Example precompiled streams: 560 or latest
Note: Precompiled streams are only supported on RHEL8 x86_64 and RHEL9 x86_64.
To uninstall or change streams on Fedora and RHEL:
sudo dnf module remove --all nvidia-driver
sudo dnf module reset nvidia-driver
For CUDA Runle:
Pass the CLI argument to the CUDA runle to opt in to NVIDIA Proprietary Kernel Modules:
45
Installation Guide for Linux, Release 12.6
sh cuda_<release>_<version>_linux.run --kernel-module-type=proprietary
46 Chapter 8. NVIDIA Proprietary Kernel Modules
Chapter 9. Precompiled Streams
Precompiled streams oer 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 les. 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 xes 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):
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Installation Guide for Linux, Release 12.6
sudo dnf module install nvidia-driver:latest
▶ <id> locks the driver updates to the specied 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 specied 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.
9.1. 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, rst 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>
48 Chapter 9. Precompiled Streams
Installation Guide for Linux, Release 12.6
9.2. Modularity Proles
Modularity proles work with any supported modularity stream and allow for additional use cases.
These modularity proles are available on RHEL and Fedora.
Table 7: Table 5. List of nvidia-driver Module Proles
Stream Prole Use Case
Default ∕
default
Installs all the driver packages in a stream.
Kickstart ∕ks Performs unattended Linux OS installation using a cong le.
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 proles 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.
9.2. Modularity Proles 49
Installation Guide for Linux, Release 12.6
50 Chapter 9. Precompiled Streams
Chapter 10. Kickstart Installation
10.1. 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 le, make sure you are using the /ks prole and :latest-
dkms stream:
@nvidia-driver:latest-dkms∕ks
4. Perform the post-installation actions.
10.2. 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 le, make sure you are using the /ks prole and :latest-
dkms stream:
@nvidia-driver:latest-dkms∕ks
4. Perform the post-installation actions.
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52 Chapter 10. Kickstart Installation
Chapter 11. Runle Installation
Basic instructions can be found in the Quick Start Guide. Read on for more detailed instructions.
This section describes the installation and conguration of CUDA when using the standalone installer.
The standalone installer is a “.run” le and is completely self-contained.
11.1. Runle Overview
The Runle installation installs the NVIDIA Driver and CUDA Toolkit via an interactive ncurses-based
interface.
The installation steps are listed below. Distribution-specic 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 Runle installation does not include support for cross-platform development. For cross-platform
development, see the CUDA Cross-Platform Environment section.
11.2. 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. Tem-
porarily adding “nomodeset” to the system’s kernel boot parameters may x 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 inter-
face 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.
53
Installation Guide for Linux, Release 12.6
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-12.4:
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 conguration le update.
The installer must be executed with sucient privileges to perform some actions. When the cur-
rent privileges are insucient 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 les 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 over-
written 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 conguration le, ∕etc∕X11∕xorg.
conf, may need to be modied. In some cases, nvidia-xconfig can be used to automatically
generate an xorg.conf le 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 le. 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.
54 Chapter 11. Runle Installation
Installation Guide for Linux, Release 12.6
11.3. Disabling Nouveau
To install the Display Driver, the Nouveau drivers must rst be disabled. Each distribution of Linux has
a dierent method for disabling Nouveau.
The Nouveau drivers are loaded if the following command prints anything:
lsmod | grep nouveau
11.3.1. Fedora
1. Create a le at ∕usr∕lib∕modprobe.d∕blacklist-nouveau.conf with the following con-
tents:
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.
11.3.2. RHEL / Rocky and KylinOS
1. Create a le 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
11.3.3. OpenSUSE
1. Create a le at ∕etc∕modprobe.d∕blacklist-nouveau.conf with the following contents:
blacklist nouveau
options nouveau modeset=0
2. Regenerate the kernel initrd:
11.3. Disabling Nouveau 55
Installation Guide for Linux, Release 12.6
sudo ∕sbin∕mkinitrd
11.3.4. SLES
No actions to disable Nouveau are required as Nouveau is not installed on SLES.
11.3.5. WSL
No actions to disable Nouveau are required as Nouveau is not installed on WSL.
11.3.6. Ubuntu
1. Create a le 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
11.3.7. Debian
1. Create a le 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
11.4. Device Node Verication
Check that the device les∕dev∕nvidia* exist and have the correct (0666) le permissions. These
les 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 les 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 les do
not exist, you can create them manually by using a startup script such as the one below:
56 Chapter 11. Runle Installation
Installation Guide for Linux, Release 12.6
#!∕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
11.4. Device Node Verication 57
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11.5. 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 in-
stallations are useful for scripting the in-
stallation of CUDA. Using this option im-
plies acceptance of the EULA. The follow-
ing ags can be used to customize the ac-
tions taken during installation. At least one
of --driver, --uninstall, and --toolkit
must be passed if running with non-root per-
missions.
--driver Install the CUDA Driver.
--toolkit Install the CUDA Toolkit.
--toolkitpath=<path> Install the CUDA Toolkit to the <path> direc-
tory. If not provided, the default path of ∕
usr∕local∕cuda-12.4 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 runle, the raw les 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 in-
staller.
Overriding Instal-
lation Checks
--override Ignores compiler, third-party library, and
toolkit detection checks which would prevent
the CUDA Toolkit from installing.
No OpenGL Li-
braries
--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-
xcong
--run-nvidia-xconfig Tells the driver installation to run nvidia-
xcong to update the system X conguration
le so that the NVIDIA X driver is used. The
pre-existing X conguration le will be backed
up.
No nvidia-drm
kernel module
--no-drm Do not install the nvidia-drm kernel mod-
ule. 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 Tempo-
rary 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 avor of kernel source when building the
NVIDIA kernel module. The kernel-open avor
is only supported on Turing GPUs and newer.
-m=kernel Tells the driver installation to use legacy
avor 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
avor of kernel source when building
the NVIDIA kernel module. The kernel-
open avor is only supported on Tur-
ing GPUs and newer. Shorthand for
--kernel-module-build-directory=kernel-open
Show Installer
Options
--help Prints the list of command-line options to std-
out.
58 Chapter 11. Runle Installation
Installation Guide for Linux, Release 12.6
11.6. 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-12.6∕bin:
sudo ∕usr∕local∕cuda-12.6∕bin∕cuda-uninstaller
To uninstall the NVIDIA Driver, run nvidia-uninstall:
sudo ∕usr∕bin∕nvidia-uninstall
To enable the Nouveau drivers, remove the blacklist le created in the Disabling Nouveau section, and
regenerate the kernel initramfs/initrd again as described in that section.
11.6. Uninstallation 59
Installation Guide for Linux, Release 12.6
60 Chapter 11. Runle Installation
Chapter 12. Conda Installation
This section describes the installation and conguration of CUDA when using the Conda installer. The
Conda packages are available at https://anaconda.org/nvidia.
12.1. Conda Overview
The Conda installation installs the CUDA Toolkit. The installation steps are listed below.
12.2. 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
12.3. Uninstalling CUDA Using Conda
To uninstall the CUDA Toolkit using Conda, run the following command:
conda remove cuda
12.4. Installing Previous CUDA Releases
All Conda packages released under a specic 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
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12.5. 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 les, 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 specic libraries you need.
62 Chapter 12. Conda Installation
Chapter 13. 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 environ-
ment.
Prerequisites
To install Wheels, you must rst 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 le, then you can add the following line to your
requirements.txt le 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-cccl-cu12
▶ nvidia-cuda-cupti-cu12
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Installation Guide for Linux, Release 12.6
▶ nvidia-cuda-nvcc-cu12
▶ nvidia-cuda-opencl-cu12
▶ nvidia-cuda-nvrtc-cu12
▶ nvidia-cublas-cu12
▶ nvidia-cuda-sanitizer-api-cu12
▶ nvidia-cut-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-cuda-runtime-cu126
▶ nvidia-cuda-cccl-cu126
▶ nvidia-cuda-cupti-cu126
▶ nvidia-cuda-nvcc-cu126
▶ nvidia-cuda-opencl-cu126
▶ nvidia-cublas-cu126
▶ nvidia-cuda-sanitizer-api-cu126
▶ nvidia-cuda-nvrtc-cu126
▶ nvidia-cut-cu126
▶ nvidia-curand-cu126
▶ nvidia-cusolver-cu126
▶ nvidia-cusparse-cu126
▶ nvidia-npp-cu126
▶ nvidia-nvfatbin-cu126
▶ nvidia-nvjitlink-cu126
▶ nvidia-nvjpeg-cu126
▶ nvidia-nvml-dev-cu126
▶ nvidia-nvtx-cu126
64 Chapter 13. Pip Wheels
Chapter 14. Tarball and Zip Archive
Deliverables
In an eort 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 avail-
able 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,
runle, 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 redis-
tribution. Instructions for developers using CMake and Bazel build systems are provided in the next
sections.
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14.1. 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
66 Chapter 14. Tarball and Zip Archive Deliverables
Installation Guide for Linux, Release 12.6
Table 8: Available Tarball and Zip Archives
Product Example
CUDA Toolkit .∕parse_redist.py --product cuda --label 12.6.0
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
14.2. Importing Tarballs into CMake
The recommended module for importing these tarballs into the CMake build system is via FindCUDA-
Toolkit (3.17 and newer).
Note: The FindCUDA module is deprecated.
The path to the extraction location can be specied with the CUDAToolkit_ROOT environmental vari-
able. 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 le and commands, see cmake/2_ExternalProject/.
14.3. 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/.
14.2. Importing Tarballs into CMake 67
Installation Guide for Linux, Release 12.6
68 Chapter 14. Tarball and Zip Archive Deliverables
Chapter 15. 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.
15.1. 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:
a. For aarch64:
sudo apt-get install cuda-cross-aarch64
b. For QNX:
sudo apt-get install cuda-cross-qnx
5. Perform the post-installation actions.
69
Chapter 16. Post-installation Actions
The post-installation actions must be manually performed. These actions are split into mandatory,
recommended, and optional sections.
16.1. Mandatory Actions
Some actions must be taken after the installation before the CUDA Toolkit and Driver can be used.
16.1.1. Environment Setup
The PATH variable needs to include export PATH=∕usr∕local∕cuda-12.6∕
bin${PATH:+:${PATH}}. Nsight Compute has moved to ∕opt∕nvidia∕nsight-compute∕ only
in rpm/deb installation method. When using .run installer it is still located under ∕usr∕local∕
cuda-12.6∕.
To add this path to the PATH variable:
export PATH=∕usr∕local∕cuda-12.6∕bin${PATH:+:${PATH}}
In addition, when using the runle installation method, the LD_LIBRARY_PATH variable needs to con-
tain ∕usr∕local∕cuda-12.6∕lib64 on a 64-bit system, or ∕usr∕local∕cuda-12.6∕lib on a 32-
bit system
▶ To change the environment variables for 64-bit operating systems:
export LD_LIBRARY_PATH=∕usr∕local∕cuda-12.6∕lib64\
${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
▶ To change the environment variables for 32-bit operating systems:
export LD_LIBRARY_PATH=∕usr∕local∕cuda-12.6∕lib\
${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
Note that the above paths change when using a custom install path with the runle installation
method.
71
Installation Guide for Linux, Release 12.6
16.2. Recommended Actions
Other actions are recommended to verify the integrity of the installation.
16.2.1. Install Persistence Daemon
NVIDIA is providing a user-space daemon on Linux to support persistence of driver state across CUDA
job runs. The daemon approach provides a more elegant and robust solution to this problem than
persistence mode. For more details on the NVIDIA Persistence Daemon, see the documentation here.
The NVIDIA Persistence Daemon can be started as the root user by running:
∕usr∕bin∕nvidia-persistenced --verbose
This command should be run on boot. Consult your Linux distribution’s init documentation for details
on how to automate this.
16.2.2. Install Writable Samples
CUDA Samples are now located in https://github.com/nvidia/cuda-samples, which includes instruc-
tions for obtaining, building, and running the samples.
16.2.3. Verify the Installation
Before continuing, it is important to verify that the CUDA toolkit can nd and communicate correctly
with the CUDA-capable hardware. To do this, you need to compile and run some of the sample pro-
grams, located in https://github.com/nvidia/cuda-samples.
Note: Ensure the PATH and, if using the runle installation method, LD_LIBRARY_PATH variables are
set correctly.
16.2.3.1 Verify the Driver Version
If you installed the driver, verify that the correct version of it is loaded. If you did not install the driver,
or are using an operating system where the driver is not loaded via a kernel module, such as L4T, skip
this step.
When the driver is loaded, the driver version can be found by executing the command
cat ∕proc∕driver∕nvidia∕version
Note that this command will not work on an iGPU/dGPU system.
72 Chapter 16. Post-installation Actions
Installation Guide for Linux, Release 12.6
16.2.3.2 Running the Binaries
After compilation, nd and run deviceQueryfrom https://github.com/nvidia/cuda-samples. If the
CUDA software is installed and congured correctly, the output for deviceQuery should look sim-
ilar to that shown in Figure 1.
Figure 1: Valid Results from deviceQuery CUDA Sample
The exact appearance and the output lines might be dierent on your system. The important out-
comes are that a device was found (the rst highlighted line), that the device matches the one on your
system (the second highlighted line), and that the test passed (the nal highlighted line).
If a CUDA-capable device and the CUDA Driver are installed but deviceQuery reports that no CUDA-
capable devices are present, this likely means that the ∕dev∕nvidia* les 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.
Note that the measurements for your CUDA-capable device description will vary from system to sys-
tem. The important point is that you obtain measurements, and that the second-to-last line (in Figure
2) conrms 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.
16.2. Recommended Actions 73
Installation Guide for Linux, Release 12.6
Figure 2: Valid Results from bandwidthTest CUDA Sample
If you run into diculties with the link step (such as libraries not being found), consult the Linux Release
Notes found in https://github.com/nvidia/cuda-samples.
16.2.4. Install Nsight Eclipse Plugins
To install Nsight Eclipse plugins, an installation script is provided:
∕usr∕local∕cuda-12.6∕bin∕nsight_ee_plugins_manage.sh install <eclipse-dir>
Refer to Nsight Eclipse Plugins Installation Guide for more details.
16.2.5. Local Repo Removal
Removal of the local repo installer is recommended after installation of CUDA SDK.
Ubuntu and Debian
sudo apt-get remove --purge "cuda-repo-<distro>-X-Y-local*"
Amazon Linux 2023 / Fedora / KylinOS / RHEL 8 / Rocky Linux 8 / RHEL 9 / Rocky Linux 9
sudo dnf remove "cuda-repo-<distro>-X-Y-local*"
Azure Linux
sudo tdnf remove "cuda-repo-<distro>-X-Y-local*"
openSUSE 15 and SLES 15
74 Chapter 16. Post-installation Actions
Installation Guide for Linux, Release 12.6
sudo zypper remove "cuda-repo-<distro>-X-Y-local*"
Removal of the local repo installer is recommended after installation of NVIDIA driver.
Ubuntu and Debian
sudo apt-get remove --purge "nvidia-driver-local-repo-<distro>*"
Amazon Linux 2023 / Fedora / KylinOS / RHEL 8 / Rocky Linux 8 / RHEL 9 / Rocky Linux 9
sudo dnf remove "nvidia-driver-local-repo-<distro>*"
Azure Linux
sudo tdnf remove "nvidia-driver-local-repo-<distro>*"
openSUSE 15 and SLES 15
sudo zypper remove "nvidia-driver-local-repo-<distro>*"
16.3. Optional Actions
Other options are not necessary to use the CUDA Toolkit, but are available to provide additional fea-
tures.
16.3.1. 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 2023 / Fedora / KylinOS 10 / RHEL 8 / Rocky Linux 8 / RHEL 9 / Rocky Linux 9
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
Debian and Ubuntu
sudo apt-get install g++ freeglut3-dev build-essential libx11-dev \
libxmu-dev libxi-dev libglu1-mesa-dev libfreeimage-dev libglfw3-dev
16.3. Optional Actions 75
Installation Guide for Linux, Release 12.6
16.3.2. Install the Source Code for cuda-gdb
The cuda-gdb source must be explicitly selected for installation with the runle installation method.
During the installation, in the component selection page, expand the component “CUDA Tools 12.4”
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-12.4∕extras directory.
16.3.3. 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 dierent 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
76 Chapter 16. Post-installation Actions
Chapter 17. Advanced Setup
Below is information on some advanced setup scenarios which are not covered in the basic instructions
above.
77
Installation Guide for Linux, Release 12.6
Table 9: Advanced Setup Scenarios when Installing CUDA
Scenario Instructions
Install CUDA using the Package Manager instal-
lation method without installing the NVIDIA GL
libraries.
Fedora
Install CUDA using the following command:
sudo dnf install cuda-toolkit-12-4 \
nvidia-driver-cuda akmod-nvidia
Follow the instructions here to ensure that Nou-
veau is disabled.
If performing an upgrade over a previous instal-
lation, the NVIDIA kernel module may need to be
rebuilt by following the instructions here.
OpenSUSE/SLES
On some system congurations the NVIDIA GL li-
braries 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-12-4 \
nvidia-computeG04 \
nvidia-gfxG04-kmp-default
Follow the instructions here to ensure that Nou-
veau is disabled.
Ubuntu
This functionality isn’t supported on Ubuntu. In-
stead, 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 Bum-
blebee 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 di-
agnostic 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 installa-
tion 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 installa-
tion 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 installa-
tion 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 installa-
tion as normal.
Use a specic GPU for rendering the display. Add or replace a Device entry in your xorg.conf
le, located at ∕etc∕X11∕xorg.conf. The De-
vice 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 dier
on a case-by-case basis. For example, if you
have two NVIDIA GPUs and you want the rst
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 resem-
ble “PCI:00:02.0” and can be found by running
lspci.
Install CUDA to a specic directory using the
Package Manager installation method.
RPM
The RPM packages don’t support custom in-
stall 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-12.4=∕new∕toolkit package.rpm
You will need to install the packages in the cor-
rect 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”
rst, 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 relo-
cated.
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 les
to the desired install location. See the next sce-
nario for more details one xtracting Deb pack-
ages.
Extract the contents of the installers. Runle
The Runle can be extracted into the stan-
dalone Toolkit and Driver Runles by using the
--extract parameter. The Toolkit standalone
Runles can be further extracted by running:
.∕runfile.run --tar mxvf
The Driver Runle 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
specic architectures for specic repositories.
This is useful when a foreign architecture has
been added, causing “404 Not Found” errors to
appear when the repository meta-data is up-
dated.
Each repository you wish to restrict to spe-
cic architectures must have its sources.
list entry modied. This is done by modify-
ing the ∕etc∕apt∕sources.list le and any
les containing repositories you wish to restrict
under the ∕etc∕apt∕sources.list.d∕ direc-
tory. Normally, it is sucient 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 reposi-
tory 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-specic packages.
For more details, see the sources.list man-
page.
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 in-
stalled. 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 reect the availability of DRM regard-
less of whether this package is installed or
not.
The runle 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 sys-
tems which use a tmpfs in memory to handle
temporary storage. In this case, the --tmpdir
command-line option should be used to instruct
the runle to use a directory with sucient
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 Fe-
dora 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 unin-
stalling a dierent 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 ag, for exam-
ple:
sudo apt-get install --verbose-versions�
,→cuda
78 Chapter 17. Advanced Setup
Chapter 18. Frequently Asked
Questions
18.1. How do I install the Toolkit in a dierent
location?
The Runle installation asks where you wish to install the Toolkit during an interactive install. If in-
stalling using a non-interactive install, you can use the --toolkitpath parameter to change the in-
stall 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 pack-
age managers. See the “Install CUDA to a specic directory using the Package Manager installation
method” scenario in the Advanced Setup section for more information.
18.2. Why do I see “nvcc: No such le 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 direc-
tory where you installed the Toolkit, usually ∕usr∕local∕cuda-12.4∕bin.
export PATH=∕usr∕local∕cuda-12.4∕bin${PATH:+:${PATH}}
79
Installation Guide for Linux, Release 12.6
18.3. Why do I see “error while loading shared
libraries: <lib name>: cannot open shared
object le: No such le 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-12.4∕lib{,64}:
export LD_LIBRARY_PATH=∕usr∕local∕cuda-12.4∕lib\
${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
18.4. 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 le. 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 le to prevent these errors.
18.5. 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 specic GPU for rendering the display”
scenario in the Advanced Setup section.
80 Chapter 18. Frequently Asked Questions
Installation Guide for Linux, Release 12.6
18.6. 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 in-
forms the package manager where to nd the actual installation packages, but will not install them.
See the Package Manager Installation section for more details.
18.7. 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
18.8. 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 in-
stalled 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 su-
cient to invoke Akmods manually and regenerate the module mapping les by running the following
commands in a virtual console, and then rebooting:
sudo akmods --force
sudo depmod
18.6. Why doesn’t the cuda-repo package install the CUDA Toolkit and Drivers? 81
Installation Guide for Linux, Release 12.6
You can reach a virtual console by hitting ctrl+alt+f2 at the same time.
18.9. 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.
18.10. How do I install an older CUDA version
using a network repo?
Depending on your system conguration, you may not be able to install old versions of CUDA using
the cuda metapackage. In order to install a specic 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.
18.11. Why does the installation on SUSE install
the Mesa-dri-nouveau dependency?
This dependency comes from the SUSE repositories and shouldn’t aect the use of the NVIDIA driver
or the CUDA Toolkit. To disable this dependency, you can lock that package with the following com-
mand:
sudo zypper al Mesa-dri-nouveau
82 Chapter 18. Frequently Asked Questions
Installation Guide for Linux, Release 12.6
18.12. 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.
18.12. How do I handle “Errors were encountered while processing: glx-diversions”? 83
Installation Guide for Linux, Release 12.6
84 Chapter 18. Frequently Asked Questions
Chapter 19. 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.
4∕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 Proler, 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.
85
Installation Guide for Linux, Release 12.6
86 Chapter 19. Additional Considerations
Chapter 20. Switching between Driver
Module Flavors
Use the following steps to switch between the NVIDIA driver legacy and open module avors 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 550.
Amazon Linux 2023 / Fedora / Kylin OS / RHEL 8 / Rocky Linux 8 / RHEL 9 / Rocky Linux 9
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
87
Installation Guide for Linux, Release 12.6
OpenSUSE
To switch from legacy to open:
sudo zypper remove nvidia-driver-G06-kmp-default
sudo zypper install --details nvidia-open-driver-G06-kmp-default
sudo zypper install --details cuda-drivers-XXX
To switch from open to legacy:
sudo zypper remove nvidia-open-driver-G06-kmp-default
sudo zypper install --details cuda-drivers-XXX
SLES
To switch from legacy to open:
sudo zypper remove nvidia-driver-G06-kmp-default nvidia-driver-G06-kmp-azure
sudo zypper install --details nvidia-open-driver-G06-kmp-default nvidia-open-driver-
,→G06-kmp-azure
sudo zypper install --details cuda-drivers-XXX
To switch from open to legacy:
sudo zypper remove nvidia-open-driver-G06-kmp-default nvidia-driver-G06-open-kmp-azure
sudo zypper install --details cuda-drivers-XXX
Note: The Azure package is only available for SLES (x86_64).
88 Chapter 20. Switching between Driver Module Flavors
Chapter 21. 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.
Amazon Linux 2023 / Fedora / Kylin OS / RHEL 8 / Rocky Linux 8 / 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
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:
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sudo apt-get autoremove
Azure Linux CM2
To remove CUDA Toolkit:
sudo tdnf remove "cuda*" "*cublas*" "*cufft*" "*cufile*" "*curand*" "*cusolver*"
,→"*cusparse*" "*gds-tools*" "*npp*" "*nvjpeg*" "nsight*" "*nvvm*"
To remove NVIDIA Drivers:
sudo tdnf remove nvidia\*
90 Chapter 21. Removing CUDA Toolkit and Driver
Chapter 22. Notices
22.1. Notice
This document is provided for information purposes only and shall not be regarded as a warranty of a
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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
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NVIDIA reserves the right to make corrections, modications, enhancements, improvements, and any
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Customer should obtain the latest relevant information before placing orders and should verify that
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NVIDIA products are sold subject to the NVIDIA standard terms and conditions of sale supplied at the
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NVIDIA makes no representation or warranty that products based on this document will be suitable for
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Installation Guide for Linux, Release 12.6
services or a warranty or endorsement thereof. Use of such information may require a license from a
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ever, NVIDIA’s aggregate and cumulative liability towards customer for the products described herein
shall be limited in accordance with the Terms of Sale for the product.
22.2. OpenCL
OpenCL is a trademark of Apple Inc. used under license to the Khronos Group Inc.
22.3. 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.
92 Chapter 22. Notices
