NVIDIA vGPU for Compute Licensing#
Configure each vGPU VM to obtain a license from the NVIDIA License System for full functionality.
Note
The NVIDIA AI Enterprise license is enforced through software when you deploy NVIDIA vGPU for Compute VMs.
When booted on a supported GPU, a vGPU for Compute VM initially operates at full capability but its performance degrades over time if the VM fails to obtain a license. The full capability of the VM is restored when the license is acquired.
Once licensing is configured, a vGPU VM automatically obtains a license from the license server when booted on a supported GPU. The VM retains the license until it is shut down and then releases the license back to the license server. Licensing settings persist across reboots and need only be modified if the license server address changes or the VM is switched to running GPU passthrough.
For more information on how to license a vGPU for Compute VM from the NVIDIA License System, including step-by-step instructions, refer to the Virtual GPU Client Licensing User Guide.
Note
For vGPU for Compute deployments, one license per vGPU assigned to a VM is enforced through software. This license is valid for up to 16 vGPU instances on a single GPU or for the assignment to a VM of one vGPU assigned all the physical GPU’s framebuffer. If multiple NVIDIA C-series vGPUs are assigned to a single VM, a separate license must be obtained for each vGPU from the NVIDIA Licensing System, regardless of whether it is a Networked or Node-Locked license.
Verifying the License Status of a Licensed NVIDIA vGPU for Compute Guest VM#
After configuring an NVIDIA vGPU for Compute client VM with a license, verify the license status by displaying the licensed product name and status.
To verify the license status of a licensed client, run nvidia-smi with the ΓÇôq or --query option from within the client VM, not the hypervisor host. If the product is licensed, the expiration date is shown in the license status.
1==============NVSMI LOG==============
2
3Timestamp : Tue Jun 17 16:49:09 2025
4Driver Version : 580.46
5CUDA Version : 13.0
6
7Attached GPUs : 2
8GPU 00000000:02:01.0
9 Product Name : NVIDIA H100-80C
10 Product Brand : NVIDIA Virtual Compute Server
11 Product Architecture : Hopper
12 Display Mode : Requested functionality has been deprecated
13 Display Attached : Yes
14 Display Active : Disabled
15 Persistence Mode : Enabled
16 Addressing Mode : HMM
17 MIG Mode
18 Current : N/A
19 Pending : N/A
20 Accounting Mode : Disabled
21 Accounting Mode Buffer Size : 4000
22 Driver Model
23 Current : N/A
24 Pending : N/A
25 Serial Number : N/A
26 GPU UUID : GPU-a1833a31-1dd2-11b2-8e58-a589b8170988
27 GPU PDI : N/A
28 Minor Number : 0
29 VBIOS Version : 00.00.00.00.00
30 MultiGPU Board : No
31 Board ID : 0x201
32 Board Part Number : N/A
33 GPU Part Number : 2331-882-A1
34 FRU Part Number : N/A
35 Platform Info
36 Chassis Serial Number : N/A
37 Slot Number : N/A
38 Tray Index : N/A
39 Host ID : N/A
40 Peer Type : N/A
41 Module Id : N/A
42 GPU Fabric GUID : N/A
43 Inforom Version
44 Image Version : N/A
45 OEM Object : N/A
46 ECC Object : N/A
47 Power Management Object : N/A
48 Inforom BBX Object Flush
49 Latest Timestamp : N/A
50 Latest Duration : N/A
51 GPU Operation Mode
52 Current : N/A
53 Pending : N/A
54 GPU C2C Mode : Disabled
55 GPU Virtualization Mode
56 Virtualization Mode : VGPU
57 Host VGPU Mode : N/A
58 vGPU Heterogeneous Mode : N/A
59 vGPU Software Licensed Product
60 Product Name : NVIDIA Virtual Compute Server
61 License Status : Licensed (Expiry: 2025-6-18 8:59:55 GMT)
62….
Installing the NVIDIA GPU Operator Using a Bash Shell Script#
A bash shell script for installing the NVIDIA GPU Operator with the NVIDIA vGPU for Compute Driver is available for download from the NVIDIA AI Enterprise Infra Collection.
Note
This approach assumes there is no vGPU for Compute Driver installed on the Guest VM. The vGPU for Compute Guest driver is installed by GPU Operator.
Refer to the GPU Operator documentation for detailed instructions on deploying the NVIDIA vGPU for Compute Driver using the bash shell script.
Installing NVIDIA AI Enterprise Applications Software#
Installing NVIDIA AI Enterprise Applications Software using Docker and NVIDIA Container Toolkit#
Prerequisites#
Before you install any NVIDIA AI Enterprise container:
Ensure your vGPU for Compute Guest VM is running a supported OS distribution.
Ensure the VM has obtained a valid vGPU for Compute license from the NVIDIA License System. Refer to Licensing vGPU VMs.
Confirm that one or more NVIDIA GPUs are available and recognized by your system.
Make sure the vGPU for Compute Guest Driver is installed correctly. You can verify this by running
nvidia-smi. If you see your GPU listed, you are ready to proceed.
Installing Docker Engine#
Refer to the official Docker Installation Guide for your vGPU for Compute Guest VM OS Linux distribution.
Installing the NVIDIA Container Toolkit#
The NVIDIA Container Toolkit enables building and running GPU-accelerated Docker containers. The toolkit includes a container runtime library and utilities to configure containers to use NVIDIA GPUs automatically. Complete documentation and frequently asked questions are available on the repository wiki. Refer to the Installing the NVIDIA Container Toolkit documentation to enable the Docker repository and install the NVIDIA Container Toolkit on the Guest VM.
Once the NVIDIA Container Toolkit is installed, to configure the Docker container runtime, refer to the Configuration documentation.
Verifying the Installation: Run a Sample CUDA Container#
Refer to the Running a Sample Workload documentation to run a sample CUDA container test on your GPU.
Accessing NVIDIA AI Enterprise Containers on NGC#
NVIDIA AI Enterprise Application Software is available through the NVIDIA NGC Catalog and is identifiable by the NVIDIA AI Enterprise Supported label.
The container image for each application or framework contains the entire user-space software stack required to run it, namely, the CUDA libraries, cuDNN, any required Magnum IO components, TensorRT, and the framework itself.
Generate an NGC API key to access the NVIDIA AI Enterprise Software in the NGC Catalog using the URL provided to you by NVIDIA.
Authenticate with Docker to NGC Registry. In your shell, run:
docker login nvcr.io Username: $oauthtoken Password: <paste-your-NGC_API_key-here> A successful login (``Login Succeeded``) lets you pull containers from NGC.
From the NVIDIA vGPU for Compute VM, browse the NGC Catalog for containers labeled NVIDIA AI Enterprise Supported.
Copy the relevant
docker pullcommand.sudo docker pull nvcr.io/nvaie/rapids-pb25h1:x.y.z-runtime
Where
x.y.zis the version of your container.Run the container with GPU access.
sudo docker run --gpus all -it --rm nvcr.io/nvaie/rapids-pb25h1:x.y.z-runtime
Where
x.y.zis the version of your container.This command launches an interactive container using the vGPUs available on the Guest VM.
Installing the NVIDIA AI Enterprise Software Components Using Podman#
You can use Podman (an alternative container runtime to Docker) for running NVIDIA AI Enterprise containers. The installation flow is similar to Docker. For more information, refer to the NVIDIA AI Enterprise: RHEL with KVM Deployment Guide.
Installing NVIDIA AI Enterprise Software Components Using Kubernetes and NVIDIA Cloud Native Stack#
NVIDIA provides the Cloud Native Stack (CNS), which is a collection of software to run cloud-native workloads on NVIDIA GPUs. NVIDIA Cloud Native Stack is based on Ubuntu and RHEL, Kubernetes, Helm, and the NVIDIA GPU and Network Operator.
Refer to this repository for a series of installation guides with step-by-step instructions based on your OS distribution. The installation guides also offer instructions to deploy an application from the NGC Catalog to validate that GPU resources are accessible and functional.