Sold by: NVIDIA
Deployed on AWS
The NVIDIA GPU-Optimized AMI is an environment for running the GPU-accelerated deep learning and HPC containers from the NVIDIA NGC catalog. The deep learning containers from NGC catalog require this AMI for GPU acceleration on AWS P5d, P4d, P3, G4dn, G5 GPU instances.
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Overview
The NVIDIA GPU-Optimized AMI is a virtual machine image for accelerating your GPU accelerated Machine Learning, Deep Learning, Data Science and HPC workloads. Using this AMI, you can spin up a GPU-accelerated EC2 VM instance in minutes with a pre-installed Ubuntu OS, GPU driver, Docker and NVIDIA container toolkit.
This AMI provides easy access to NVIDIA's NGC Catalog, a hub for GPU-optimized software, for pulling & and running performance-tuned, tested, and NVIDIA certified docker containers. The NGC catalog provides free access to containerized AI, Data Science, and HPC applications, pre-trained models, AI SDKs and other resources to enable data scientists, developers, and researchers to focus on building and deploying solutions.
This GPU-optimized AMI is free with an option to purchase enterprise support offered through NVIDIA AI Enterprise. For how to get support for this AMI, scroll down to 'Support Information'
NVIDIA GPU-Optimized AMI includes:
- Ubuntu Server OS
- NVIDIA Driver
- Docker-ce
- NVIDIA Container Toolkit
- AWS CLI, NGC CLI
- Miniconda, JupyterLab, Git
Highlights
- Provides data scientists and developers fast and easy access to NVIDIA H100, A100, A10 and T4 GPUs in the cloud and GPU-optimized AI/HPC software in an environment that is fully certified by NVIDIA.
- Optimized for highest performance across a wide range of workloads on NVIDIA GPUs
- NVIDIA accelerates innovation by eliminating the complex do-it-yourself task of building and optimizing a complete deep learning software stack tuned specifically for GPUs.
Details
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Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
Ubuntu 24.04 x86_64 with NVIDIA Drivers
Deployed on AWS
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Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
This product is available free of charge. Free subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimensions summary
You pay nothing for this software. It is offered free of charge across all listed options. Pricing is organized by AWS EC2 GPU instance type, spanning the g4dn, g5, p3, p3dn, p4d, and p5 families, all billed hourly. Each option maps to a different instance size and GPU configuration, so your cost comes from the AWS compute you run, not the AMI itself. You choose the instance that fits your GPU, CPU, and memory needs. Larger instances carry higher AWS compute charges, while the AMI stays free with an enterprise support option available.
Top-of-mind questions for buyers
Each option maps to an AWS EC2 GPU instance. The g4dn family uses NVIDIA T4 GPUs, p3 uses V100 GPUs, and p4d uses A100 GPUs. Larger names like g4dn.12xlarge or p5.48xlarge carry more GPUs, CPU, and memory. You pick the size that fits your workload.
The AMI software is free of charge, so no software fee applies at any time. When you stop or terminate an instance, AWS compute charges stop too. Stopped instances may still incur AWS storage fees for attached EBS volumes, but the AMI itself never adds cost.
The AMI adds no software charge. Your bill comes entirely from the AWS EC2 compute you run, metered by instance-hours. Options with more or higher-end GPUs bill at higher AWS compute rates. You also pay separately for any EBS storage volumes you attach for datasets.
docs.nvidia.com+1
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Vendor refund policy
This AMI is provided free of charge.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
NVIDIA Enterprise AI VMI 2026.4.1 release with NVIDIA driver 595.58.03
Additional details
Usage instructions
For usage instructions and quick start guide, please refer: https://docs.nvidia.com/ngc/ngc-deploy-public-cloud/ngc-aws/index.html
Resources
Support
Vendor support
This AMI comes with an Enterprise Support option https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/
For more information please check https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/
Free support for AWS images is available through forums, technical documentation & FAQs https://devtalk.nvidia.com/default/board/200/nvidia-gpu-cloud-ngc-users/ NVIDIA Developer Forum
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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Customer reviews
CS
Install Drivers
Reviewed on Jun 05, 2024
Review from a verified AWS customer
You need to first change the username from root to ubuntu in order to have the drivers be installed! I feel this should have been more specified in the directions!
yikesawjeez
AMI is not configured as advertised.
Reviewed on Mar 15, 2024
Review from a verified AWS customer
None of the advertised utilities are installed in the AMI, neither is CUDA. This is current as of 3/14/24. It appears to be a raw installation of 22.04, by my estimation.
root@ip-172-31-38-109:/cuda-samples/Samples/5_Domain_Specific/nbody# jupyterlab --version/cuda-samples/Samples/5_Domain_Specific/nbody# miniconda --version
jupyterlab: command not found
root@ip-172-31-38-109:
miniconda: command not found
There's been a lot of troubleshooting so far with regard to attempting to get cuda installed, so I won't copy-paste my terminal.
Dan
Drives auto installed on login not boot
Reviewed on Feb 15, 2024
Review from a verified AWS customer
I wanted to use this AMI in my automation to run ML jobs in our platform. What I needed was a Ubuntu 22.04, because podman is in the repo, and Nvidia drivers installed. The downside of this AMI is, Nvidia drivers are installed via /home/ubuntu/.bashrc and not cloud-init. I looked at /var/tmp/nvidia/driver.sh and there was no variable to set to force driver install at cloud-init. Since my automation runs at the end of cloud-init this doesn't work.
Ema
Very good
Reviewed on Jan 20, 2024
Review from a verified AWS customer
Older reviews are not valid anymore, now at the date of my review the image is very good, it has all the drivers required to run optimized code on various types of NVIDIA GPUs, it has CUDA 12.1 preinstalled and also miniconda and Jupyterlab.
The machine is ready to run code on GPU very easily with everything you need already in place.
AI researcher unhappy with NVIDIA software
Missing drivers
Reviewed on Dec 18, 2023
Review from a verified AWS customer
This should be preconfigured to run NVIDIA GPU Cloud (NGC) containers such as the PyTorch one, however it fails on launch on AWS (on a p3.2xlarge instance).
After sshing in, I see this error message:
<br/>Installing drivers ...<br/>modprobe: FATAL: Module nvidia not found in directory /lib/modules/6.2.0-1011-aws<br/>
And sure enough, running containers such as PyTorch (https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch ) does not work:
<br/>~$ docker run --gpus all -it --rm nvcr.io/nvidia/pytorch:23.11-py3<br/>docker: Error response from daemon: failed to create task for container: failed to create shim task: OCI runtime create failed: runc create failed: unable to start container process: error during container init: error running hook #0: error running hook: exit status 1, stdout: , stderr: Auto-detected mode as 'legacy'<br/>nvidia-container-cli: initialization error: nvml error: driver not loaded: unknown.<br/>