Overview

This article suggests how to install NVIDIA Data Center GPU Driver, CUDA Toolkit, NVIDIA Container Toolkit and other NVIDIA software directly from NVIDIA repository on NVIDIA GPU EC2 instances running Ubuntu on AWS.

Note that by using this method, you agree to NVIDIA Driver License Agreement, End User License Agreement and other related license agreement. If you are doing development, you may want to register for NVIDIA Developer Program.

This article applies to Ubuntu Linux on AWS only. Similar articles are available for AL2, AL2023, RHEL/Rocky Linux/AlmaLinux and Windows.

This article install NVIDIA Tesla driver which does not support G6f instances with fractional GPUs. Refer to this article about NVIDIA GRID driver install.

Other Options

If you need AMIs preconfigured with NVIDIA GPU driver, CUDA, other NVIDIA software, and optionally PyTorch or TensorFlow framework, consider AWS Deep Learning AMIs. Refer to Release notes for DLAMIs for currently supported options, and Deep Learning graphical desktop on Ubuntu Linux with AWS Deep Learning AMI (DLAMI) for graphical desktop setup guidance.

Refer to NVIDIA drivers for your Amazon EC2 instance for NVIDIA driver install options and NVIDIA Driver Installation Guide for Tesla driver installation instructions. You can also install NVIDIA driver from Ubuntu repository.

For container workloads, consider Amazon ECS-optimized Linux AMIs and Amazon EKS optimized AMIs

Note: instructions in this article are not applicable to pre-built AMIs.

About CUDA toolkit

As CUDA driver is part of NVIDIA GPU driver, CUDA Toolkit is generally optional when GPU instance is used to run applications (as opposed to develop applications) as the CUDA application typically packages (by statically or dynamically linking against) the CUDA runtime and libraries needed.

System Requirements

NVIDIA CUDA supports the following platforms

• Ubuntu Linux 24.04 (x86_64 and arm64)
• Ubuntu Linux 22.04 (x86_64 and arm64)

Refer to Driver installation guide for supported kernel versions, compilers and libraries.

Prerequisites

Go to Service Quotas console of your desired Region to verify On-Demand Instance quota value of your desired instance type:

• G instance types: Running On-Demand G and VT instances
• P instance types: Running On-Demand P instances

Request quota increase if the assigned value is less than vCPU count of your desired EC2 instance size. Do not proceed until your applied quota value is equal or higher than your instance type vCPU count

Prepare Ubuntu Linux

Launch a new NVIDIA GPU instance running Ubuntu Linux preferably with at least 20 GB storage and connect to the instance

Update OS, and install DKMS, kernel headers and development packages

sudo apt update
sudo apt upgrade -y
sudo apt autoremove -y
sudo apt install -y dkms linux-headers-aws linux-modules-extra-aws amazon-ec2-utils unzip gcc make libglvnd-dev pkg-config

Restart your EC2 instance if kernel is updated

sudo reboot

Add NVIDIA repository

Configure Network Repo installation

DISTRO=$(. /etc/os-release;echo $ID$VERSION_ID | sed -e 's/\.//g')
if (arch | grep -q x86); then
  ARCH=x86_64
else
  ARCH=sbsa
fi
cd /tmp
curl -L -O https://developer.download.nvidia.com/compute/cuda/repos/$DISTRO/$ARCH/cuda-keyring_1.1-1_all.deb
sudo apt install -y ./cuda-keyring_1.1-1_all.deb
sudo apt update 

If you are installing from AWS China Region, you may be able to replace repository source from https://developer.download.nvidia.com to https://developer.download.nvidia.cn

if (ec2-metadata -z | grep cn-); then
  sudo sed -i "s/nvidia\.com/nvidia\.cn/g" /etc/apt/sources.list.d/cuda-ubuntu*.list
  sudo apt clean
fi

Install NVIDIA Driver

Option 1: NVIDIA repo driver

To install latest Tesla driver from NVIDIA repository

sudo apt install -y nvidia-open 
sudo apt install -y nvidia-xconfig

To install a specific driver branch before R590, e.g. R580 LTSB

sudo apt install -y nvidia-open-580
sudo apt install -y nvidia-xconfig

NVIDIA has removed branch designation from the package name starting from R590. Refer to Version Locking if you want to pin NVIDIA driver branch.

The above install open-source GPU kernel module which is recommended by NVIDIA (and is different from Nouveau open-source driver). Refer to Driver Installation Guide about NVIDIA Kernel Modules and installation options.

Option 2: Ubuntu repo driver

Alternatively, pre-compiled NVIDIA modules may be available from Ubuntu repository.

sudo apt update
VERSION=$(apt-cache search "nvidia-driver" | grep "^nvidia-driver-.*-server-open" | cut -d"-" -f3 | sort -r | head -1)
sudo apt install -y linux-modules-nvidia-$VERSION-server-open-aws nvidia-headless-no-dkms-$VERSION-server-open nvidia-driver-$VERSION-server-open nvidia-utils-$VERSION-server
sudo apt install -y nvidia-settings

P instance

If you are using a P instance with multiple GPUs, you may need to install Fabric Manager. Refer to UFM (Unified Fabric Manager) section below for details.

Optional: Compute-only and Desktop Installation

NVIDIA repo supports custom installation method which supports the following configurations:

• Desktop: Contains all the X/Wayland drivers and libraries to allow running a GPU with power management enabled on a desktop system but does not include any CUDA component
• Compute-only / headless: Contains everything required to run CUDA applications on a GPU system where the GPU is not used to drive a display
• Desktop and Compute: canonical way of installing the driver, with every possible library and display component. This might be required in cross functional combinations, for CUDA-accelerated video encoding/decoding.

To install for the above cases:

• Desktop only: sudo apt install -y libnvidia-gl nvidia-dkms-open
• Compute-only/headless: sudo apt install -y libnvidia-compute nvidia-dkms-open
• Desktop and Compute: sudo apt install -y nvidia-open

Refer to NVIDIA Driver Installation Guide for more information.

Verify

Restart your instance

nvidia-smi

Output should be similar to below

Mon Dec 22 00:24:37 2025       
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 590.44.01              Driver Version: 590.44.01      CUDA Version: 13.1     |
+-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA A10G                    On  |   00000000:00:1E.0 Off |                    0 |
|  0%   30C    P8             10W /  300W |       0MiB /  23028MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|  No running processes found                                                             |
+-----------------------------------------------------------------------------------------+

Refer to section Verify installation integrity on steps to verify CUDA driver integrity.

Optional: CUDA Toolkit

To install latest CUDA Toolkit

sudo apt install -y cuda-toolkit

To install a specific series, e.g. 12.x

sudo apt install -y cuda-toolkit-12

To install a specific version, e.g. 12.9

sudo apt install -y cuda-toolkit-12-9

Refer to CUDA Toolkit documentation about supported platforms and installation options.

Verify

/usr/local/cuda/bin/nvcc -V

Output should be similar to below

nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2025 NVIDIA Corporation
Built on Fri_Nov__7_07:23:37_PM_PST_2025
Cuda compilation tools, release 13.1, V13.1.80
Build cuda_13.1.r13.1/compiler.36836380_0

Post-installation Actions

Refer to NVIDIA CUDA Installation Guide for Linux for post-installation actions before CUDA Toolkit can be used. For example, you may want to modify your PATH and LD_LIBRARY_PATH environment variables to include /usr/local/cuda/bin and /usr/local/cuda/lib64 respectively

sed -i '$aexport PATH=$PATH:/usr/local/cuda/bin' ~/.bashrc
sed -i '$aexport LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64' ~/.bashrc
. ~/.bashrc

Optional: NVIDIA Container Toolkit

NVIDIA Container toolkit supports Ubuntu on both x86_64 and arm64. For arm64, use g5g.2xlarge or larger instance size as g5g.xlarge may cause failures due to the limited system memory.

To install latest NVIDIA Container Toolkit

sudo apt install -y nvidia-container-toolkit

Refer to NVIDIA Container toolkit documentation about supported platforms, prerequisites and installation options

Verify

nvidia-container-cli -V

Output should be similar to below

cli-version: 1.18.1
lib-version: 1.18.1
build date: 2025-11-24T14:45+00:00
build revision: 889a3bb5408c195ed7897ba2cb8341c7d249672f
build compiler: x86_64-linux-gnu-gcc-7 7.5.0
build platform: x86_64
build flags: -D_GNU_SOURCE -D_FORTIFY_SOURCE=2 -DNDEBUG -std=gnu11 -O2 -g -fdata-sections -ffunction-sections -fplan9-extensions -fstack-protector -fno-strict-aliasing -fvisibility=hidden -Wall -Wextra -Wcast-align -Wpointer-arith -Wmissing-prototypes -Wnonnull -Wwrite-strings -Wlogical-op -Wformat=2 -Wmissing-format-attribute -Winit-self -Wshadow -Wstrict-prototypes -Wunreachable-code -Wconversion -Wsign-conversion -Wno-unknown-warning-option -Wno-format-extra-args -Wno-gnu-alignof-expression -Wl,-zrelro -Wl,-znow -Wl,-zdefs -Wl,--gc-sections

Container engine configuration

Refer to NVIDIA Container Toolkit documentation about container engine configuration.

Install and configure Docker

To install and configure docker

sudo apt install -y docker.io
sudo usermod -aG docker ubuntu
sudo systemctl enable docker

sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker

Verify Docker engine configuration

To verify docker configuration

sudo docker run --rm --runtime=nvidia --gpus all public.ecr.aws/ubuntu/ubuntu:latest nvidia-smi

Output should be similar to below

Unable to find image 'public.ecr.aws/ubuntu/ubuntu:latest' locally
latest: Pulling from ubuntu/ubuntu
16c195d4c5e9: Pull complete 
Digest: sha256:70c941f44c475633b5968e549eea587e78de9d60166408c4ffcd87a3e30ec713
Status: Downloaded newer image for public.ecr.aws/ubuntu/ubuntu:latest
Mon Dec 22 00:25:41 2025       
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 590.44.01              Driver Version: 590.44.01      CUDA Version: 13.1     |
+-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA A10G                    On  |   00000000:00:1E.0 Off |                    0 |
|  0%   30C    P8             11W /  300W |       0MiB /  23028MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|  No running processes found                                                             |
+-----------------------------------------------------------------------------------------+

EC2 Install Script

You can use the below as install script (or user data) to install GPU driver and NVIDIA Container Toolkit on a new Ubuntu NVIDIA GPU instance preferably with latest patches applied and at least 20 GB storage.

Remove # character (except the first line) if you wish to install CUDA toolkit

#!/bin/bash
export DEBIAN_FRONTEND=noninteractive
sudo apt update
sudo apt upgrade -y
sudo apt autoremove -y

sudo apt install -y dkms linux-headers-aws linux-modules-extra-aws amazon-ec2-utils unzip gcc make libglvnd-dev pkg-config

DISTRO=$(. /etc/os-release;echo $ID$VERSION_ID | sed -e 's/\.//g')
if (arch | grep -q x86); then
  ARCH=x86_64
else
  ARCH=sbsa
fi
cd /tmp
curl -L -O https://developer.download.nvidia.com/compute/cuda/repos/$DISTRO/$ARCH/cuda-keyring_1.1-1_all.deb
sudo apt install -y ./cuda-keyring_1.1-1_all.deb
sudo apt update

sudo apt install -y nvidia-open
sudo apt install -y nvidia-xconfig

USER=ubuntu

# sudo apt install -y cuda-toolkit
# sed -i '$aexport PATH=$PATH:/usr/local/cuda/bin' /home/$USER/.bashrc
# sed -i '$aexport LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64' /home/$USER/.bashrc

sudo apt install -y docker.io
sudo usermod -aG docker $USER
sudo systemctl enable docker

sudo apt install -y nvidia-container-toolkit
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker

if ( ec2-metadata -t | grep -q " p[0-9]" ); then
  sudo apt install -y nvidia-fabricmanager libnvidia-nscq libnvsdm nvidia-imex
  if ( ec2-metadata -t | grep -q " p[6-9]" ); then
    sudo apt install -y nvlsm infiniband-diags
    echo "ib_umad" | sudo tee -a /etc/modules-load.d/modules.conf
    sudo modprobe ib_umad
  fi
  sudo systemctl enable --now nvidia-fabricmanager
fi


sudo reboot

Verify

Connect to your EC2 instance.

nvidia-smi
nvidia-container-cli -V
sudo docker run --rm --runtime=nvidia --gpus all public.ecr.aws/ubuntu/ubuntu:latest nvidia-smi

If used as user data, view /var/log/cloud-init-output.log to troubleshoot any installation issues.

Perform post-installation actions in order to use CUDA toolkit (if installed).

Verify installation integrity

NVIDIA driver and NVIDIA Container Toolkit

To verify integrity of installation, you can use CUDA samples container image to validate CUDA driver.

sudo docker run --rm --runtime=nvidia --gpus all nvcr.io/nvidia/k8s/cuda-sample:devicequery

Ensure you get Result = PASS output.

NVIDIA driver and CUDA Toolkit

If CUDA toolkit is installed, you can download, compile and run CUDA samples such as deviceQuery.

If you are using a P instance with multiple GPUs, you may need to install Fabric Manager. Refer to UFM (Unified Fabric Manager) section below for instructions

GUI (graphical desktop) remote access

If you need remote graphical desktop access, refer to Install GUI (graphical desktop) on Amazon EC2 instances running Ubuntu Linux

This article installs NVIDIA Tesla driver (also know as NVIDIA Datacenter Driver), which is intended primarily for GPU compute workloads. If configured in xorg.conf, Tesla drivers support one display of up to 2560x1600 resolution.

GRID drivers provide access to four 4K displays per GPU and are certified to provide optimal performance for professional visualization applications. Refer to GPU-accelerated graphical desktop on Ubuntu Linux with NVIDIA GRID and Amazon DCV for setup guidance.

Other software

AWS CLI

To install AWS CLI (AWS Command Line Interface) v2 through Snap

sudo snap install aws-cli --classic

Verify

aws --version

Output should be similar to below

aws-cli/2.27.53 Python/3.13.4 Linux/6.8.0-1029-aws exe/x86_64.ubuntu.24

cuDNN (CUDA Deep Neural Network library)

To install cuDNN for the latest available CUDA version.

sudo apt install -y zlib1g cudnn

Refer to cuDNN documentation about installation options and support matrix

NCCL (NVIDIA Collective Communication Library)

To install latest NCCL

sudo apt install -y libnccl2 libnccl-dev

Refer to NCCL documentation about installation options

DCGM (Data Center GPU Manager)

To install DCGM

CUDA_VERSION=$(nvidia-smi | sed -E -n 's/.*CUDA Version: ([0-9]+)[.].*/\1/p')
sudo apt-get install --yes \
                       --install-recommends \
                       datacenter-gpu-manager-4-cuda${CUDA_VERSION}

Refer to DCGM documentation for more information

Verify

dcgmi --version

Output should be similar to below

dcgmi  version: 4.4.2

GDS (GPUDirect Storage)

To install NVIDIA Magnum IO GPUDirect® Storage (GDS)

sudo apt install -y nvidia-gds

To install for a specific CUDA version, e.g. 13.0

sudo apt install -y nvidia-gds-13-0

Reboot

Restart to load kernel module

sudo reboot

Verify

To verify module

lsmod | grep nvidia_fs

Output should be similar to below

nvidia_fs             262144  0
nvidia              11481088  3 nvidia_uvm,nvidia_fs,nvidia_modeset

To verify successful installation

sudo /usr/local/cuda/gds/tools/gdscheck -p

Output should be similar to below

 GDS release version: 1.16.0.49       
 nvidia_fs minimum version: 2.12      
 Platform: x86_64  
...
...
 =========                       
 GPU INFO:                      
 =========           
 GPU index 0 NVIDIA A10G bar:1 bar size (MiB):32768 supports GDS, IOMMU State: Disabled          
 ==============                            
 PLATFORM INFO:              
 ==============                    
 IOMMU: disabled
 Nvidia Driver Info Status: Supported(Nvidia Open Driver Installed)
 Cuda Driver Version Installed:  13010
 Platform: g5.xlarge, Arch: x86_64(Linux 6.14.0-1018-aws)
 Platform verification succeeded

Refer to GDS documentation and Driver installation guide for more information

GDRCopy

Magnum IO GDRCopy packages for different CUDA versions can be installed from NVIDIA Developer download site. Alternatively, download and compile from Github

Restart your EC2 instance

sudo reboot

Verify

lsmod | grep gdr

Output should be similar to below

gdrdrv                 28672  0
nvidia              14376960  7 nvidia_uvm,gdrdrv,nvidia_modeset

CUDA-X Libraries

NVIDIA repository also provides access to CUDA Math, Quantum and other libraries such as cuTENSOR, cuFFT and cuQuantum. Refer to NVIDIA site for more information

UFM (Unified Fabric Manager)

Eligibility

To determine if you need NVIDIA Unified Fabric Manager (UFM)

nvidia-smi -q -i 0 | grep Fabric -A2 | grep State

If State is N/A, you do not need Fabric Manager

        State                             : N/A

If State is not N/A, install Fabric Manager as per next section

        State                             : In Progress

Install

To install latest NVIDIA Unified Fabric Manager (UFM), NSCQ, NVSDM, IMEX for EC2 instances with NVIDIA NVLink.

sudo apt install -y nvidia-fabricmanager libnvidia-nscq libnvsdm nvidia-imex
sudo systemctl enable --now nvidia-fabricmanager

P6 instance

P6 instance requires NVLink Subnet Manager (NVLSM)

sudo apt install -y nvlsm infiniband-diags
echo "ib_umad" | sudo tee -a /etc/modules-load.d/modules.conf
sudo modprobe ib_umad

sudo systemctl restart nvidia-fabricmanager

Refer to EC2 and NVIDIA documentation for up to date instructions.

Verify

nv-fabricmanager -v
systemctl status nvidia-fabricmanager

Output should be similar to below

Fabric Manager version is : 590.44.01

● nvidia-fabricmanager.service - NVIDIA fabric manager service
     Loaded: loaded (/usr/lib/systemd/system/nvidia-fabricmanager.service; enabled; preset: enabled)
     Active: active (running) since ......... UTC; 1min 4s ago
    Process: 22851 ExecStart=/usr/bin/nvidia-fabricmanager-start.sh --mode start (code=exited, status=0/SUCCESS)
   Main PID: 22881 (nv-fabricmanage)
      Tasks: 18 (limit: 3355442)
     Memory: 38.1M
        CPU: 633ms
     CGroup: /system.slice/nvidia-fabricmanager.service
             └─22881 /usr/bin/nv-fabricmanager -c /usr/share/nvidia/nvswitch/fabricmanager.cfg
.........compute.internal nv-fabricmanager[22881]: Starting nvidia-fabricmanager.service - NVIDIA fabric manager service...
.........compute.internal nv-fabricmanager[22881]: Detected Pre-NVL5 system
.........compute.internal nv-fabricmanager[22881]: Connected to 1 node.
.........compute.internal nv-fabricmanager[22881]: Successfully configured all the available NVSwitches to route GPU NVLink traffic. NVLink Peer-to-Peer support will be enabled once the GPUs are successfully registered with the NVLink fabric.
.........compute.internal nv-fabricmanager[22881]: Started "Nvidia Fabric Manager"
.........compute.internal nv-fabricmanager[22881]: Started nvidia-fabricmanager.service - NVIDIA fabric manager service.

To view GPU fabric registration status

nvidia-smi -q -i 0 | grep -i -A 2 Fabric

Output should be similar to below after the GPUs have successfully registered

    Fabric
        State                             : Completed
        Status                            : Success

Refer to Fabric Manager documentation for more information.