NVIDIA AI Enterprise offers the flexibility run AI workloads within VMs but if you want to embrace containers, upstream Kubernetes is also offered. VMware vSphere® with VMware Tanzu® support is available with NVIDIA AI Enterprise 1.1 or later and vSphere 7 Update 3c.

By leveraging Kubernetes, the IT Administrator can automate deployments, scale and manage containerized AI applications and frameworks.

Kubernetes is an open-source container orchestration platform that makes the job of a DevOps engineer easier. Applications can be deployed on Kubernetes as logical units which are easy to manage, upgrade and deploy with zero downtime (rolling upgrades) and high availability using replication. Deploying Triton Inference Server on Kubernetes offers these same benefits to AI in the Enterprise. To easily manage GPU resources in the Kubernetes cluster, the NVIDIA GPU operator is leveraged.

NVIDIA AI Enterprise 1.1 or later

Enterprises can orchestrate AI within their Data Center by leveraging VM’s with Kubernetes. This deployment option provides IT Administrators with the benefits of virtualization as well as the benefits of container orchestration such as simplifying the scaling out training jobs and autoscaling inferencing requests, which makes it ideal for a production deployment in the final stages of the AI development lifecycle. This option is also useful for AI Practitioners who require a specific flavor of Kubernetes or if their applications have Kubernetes related dependencies. But using Kubernetes alone stops at the VM guest OS, the underlaying NVIDIA GPU hardware is abstracted away causing restrictions on resource declaration which increases complexity. With vSphere with Tanzu there is a direct integration with vSphere which provides a complete orchestration solution.


vSphere with Tanzu is declarative so creating and interacting with a GPU enabled cluster often requires fewer steps than upstream Kubernetes, and it can be done on-demand. This allows you to create and operate Tanzu Kubernetes clusters natively in vSphere with Tanzu.

Helm is an application package manager running on top of Kubernetes. Helm is very similar to what Debian/RPM is for Linux, or what JAR/WAR is for Java-based applications. Helm charts help you define, install, and upgrade even the most complex Kubernetes applications.

The NVIDIA Network Operator leverages Kubernetes custom resources and the Operator framework to configure fast networking, RDMA, and GPUDirect. The Network Operator’s goal is to install the host networking components required to enable RDMA and GPUDirect in a Kubernetes cluster. It does so by configuring a high-speed data path for IO intensive workloads on a secondary network in each cluster node.


The GPU Operator allows DevOps Engineers of Kubernetes clusters to manage GPU nodes just like CPU nodes in the cluster. Instead of providing a special OS image for GPU nodes, administrators can deploy a standard OS image for both CPU and GPU nodes and then rely on the GPU Operator to provide the required software components for GPUs.


The GPU Operator is packaged as a Helm Chart. It installs and manages the lifecycle of software components so GPU accelerated applications can be run on Kubernetes.

The components are as follows:

  • GPU Feature Discovery, which labels the worker node based on the GPU specs. This enables customers to more granularly select the GPU resources that their application requires.

  • The NVIDIA AI Enterprise Guest Driver

  • Kubernetes Device Plugin, which advertises the GPU to the Kubernetes scheduler

  • NVIDIA Container Toolkit – allows users to build and run GPU accelerated containers. The toolkit includes a container runtime library and utilities to automatically configure containers to leverage NVIDIA GPUs.

  • DCGM Monitoring – Allows monitoring of GPUs on Kubernetes.

How does the GPU Operator help IT Infrastructure Teams?

GPU Operator enables DevOps teams to manage the lifecycle of GPUs when used with Kubernetes at a Cluster level. There is no need to manage each node individually. Without GPU Operators, infrastructure teams had to manage two operating system images, one for GPU nodes and one CPU node. When using the GPU Operator, infrastructure teams can use a CPU image with GPU worker nodes. It allows customers to run GPU accelerated applications on immutable operating systems as well. Faster node provisioning is achievable since the GPU Operator has been built in a way that it detects newly added GPU accelerated Kubernetes worker nodes. Then automatically installs all software components required to run GPU accelerated applications. The GPU Operator is a single tool to manage all K8s components (GPU Device Plugin, GPU Feature Discovery, GPU Monitoring Tools, NVIDIA Runtime). It is important to note, GPU Operator installs NVIDIA AI Enterprise Guest Driver as well.

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