Prerequisites and Features

Prerequisites and features are additional infrastructure required by a tenant’s NVIDIA Run:ai cluster. Prerequisites are components every tenant cluster depends on, such as the GPU Operator. Features extend NVIDIA Run:ai to support specific workload types, such as distributed training or inference, and only need to be enabled for the workloads that tenant plans to run. The runai-installer CLI installs and configures these for you, alongside the cluster component itself.

Custom Helm Values

For settings not exposed directly by the installer, use customHelmValues to merge custom Helm values into the relevant chart. cluster.customHelmValues accepts any key documented in Advanced Cluster Configurations - for example, GPUNetworkAccelerationEnabled for Multi-Node NVLink.

Each prerequisite or feature’s customHelmValues accepts keys from that component’s own upstream Helm chart (GPU Operator, Prometheus, Knative, and so on) - see that component’s own chart documentation, linked in its section below, for available values.

Prerequisites

Each prerequisite is configured under prerequisites in the configuration file, with a mode field controlling how the installer handles it.

• install (default) - Installs and configures the component for you.
• skip - Does not install or check for the component. Use this when the component is already installed on the cluster, or when the prerequisite does not apply to your environment (for example, the Network Operator on a cluster without RDMA or Multi-Node NVLink nodes).

Each prerequisite also accepts its own version field - omit it to install the latest version, or see Support Matrix for the supported version range if you want to set it explicitly.

NVIDIA GPU Operator

The NVIDIA GPU Operator provisions and manages GPUs on worker nodes, and is required for a tenant cluster to run AI workloads.

• The installer uses the default namespace for your platform - gpu-operator on Kubernetes, nvidia-gpu-operator on OpenShift. Otherwise, you must specify the target namespace using the flag clusterConfig.global.nvidiaDcgmExporter.namespace as described in Advanced Cluster Configurations.
• NVIDIA drivers may already be installed on the nodes. In such cases, use the NVIDIA GPU Operator flags --set driver.enabled=false. DGX OS is one such example as it comes bundled with NVIDIA Drivers.
• For distribution-specific requirements and additional instructions, see the sections below:

apiVersion: helm.cattle.io/v1
kind: HelmChart
metadata:
  name: gpu-operator
  namespace: kube-system
spec:
  repo: https://helm.ngc.nvidia.com/nvidia
  chart: gpu-operator
  version: v25.3.4
  targetNamespace: gpu-operator
  createNamespace: true
  valuesContent: |-
    toolkit:
      env:
      - name: CONTAINERD_SOCKET
        value: /run/k3s/containerd/containerd.sock

For troubleshooting information, see the NVIDIA GPU Operator Troubleshooting Guide.

NVIDIA Network Operator

When deploying on clusters with RDMA or Multi-Node NVLink-capable nodes (for example B200, GB200), the NVIDIA Network Operator is required to enable high-performance networking features such as GPUDirect RDMA in Kubernetes.

The Network Operator works alongside the NVIDIA GPU Operator to provide:

• NVIDIA networking drivers for advanced network capabilities.
• Kubernetes device plugins to expose high-speed network hardware to workloads.
• Secondary network components to support network-intensive applications.

Setting prerequisites.networkOperator.mode: install installs the Network Operator:

• Kubernetes - Installs the Network Operator Helm chart.
• OpenShift - Installs the Network Operator via OperatorHub (OLM), along with the SR-IOV Network Operator and the Node Feature Discovery (NFD) Operator, if not already present.

To configure SR-IOV InfiniBand support on Kubernetes, set the required values via customHelmValues - see Network Operator Deployment with an SR-IOV InfiniBand Network for which values to set. This does not apply to OpenShift - configure SR-IOV support via the SR-IOV Network Operator directly.

NVIDIA Dynamic Resource Allocation (DRA) Driver

When deploying on clusters with Multi-Node NVLink (e.g. GB200), the NVIDIA DRA driver is required.

How the DRA driver is installed depends on the GPU Operator mode. A cluster runs in one mode only:

• ClusterPolicy mode (default) - GPUs are exposed through the NVIDIA device plugin. Install the DRA driver separately. Refer to Support Matrix for the supported DRA driver versions.
• GPUCluster mode (GPU Operator 26.7, new installations only) - The GPU Operator deploys the DRA driver, which replaces the device plugin.

For details, refer to NVIDIA DRA driver and, on OpenShift, Installing the NVIDIA GPU Operator with DRA on OpenShift.

By default, the installer installs the GPU Operator in ClusterPolicy mode and does not install the DRA driver. To install the GPU Operator in GPUCluster mode:

• Kubernetes - Set the following values in prerequisites.gpuOperator.customHelmValues. Use flat dotted keys, as shown - nested YAML is not supported. If the NVIDIA driver is pre-installed on the nodes, replace driver.nvidiaDriverCRD.enabled: true with driver.enabled: false.

  prerequisites:
    gpuOperator:
      mode: install
      customHelmValues:
        gpuCluster.deployCR: true
        clusterPolicy.deployCR: false
        driver.nvidiaDriverCRD.enabled: true

• OpenShift - The installer always creates a ClusterPolicy. Set prerequisites.gpuOperator.mode to skip and install the GPU Operator yourself, following Installing the NVIDIA GPU Operator with DRA on OpenShift.

After the DRA driver is installed, update the cluster configuration using the anyworkload-controller.GPUNetworkAccelerationEnabled flag to enable GPU network acceleration. This triggers an update of the NVIDIA Run:ai anyworkload-controller deployment and restarts the controller. Set it via cluster.customHelmValues in the installer’s configuration file - see Custom Helm Values above - or, outside the guided installer, via Helm or runaiconfig as described in Advanced Cluster Configurations.

Prometheus

NVIDIA Run:ai requires Prometheus on the tenant’s Kubernetes cluster. The setup has two parts:

• Prerequisite: Kube-Prometheus Stack - On Kubernetes, install the Kube-Prometheus Stack to provide the Prometheus Operator and the Prometheus Custom Resource Definitions (CRDs). OpenShift includes equivalent components through its built-in Cluster Monitoring Operator, so no install is required. For RKE2, see Enable Monitoring instead.
• NVIDIA Run:ai Prometheus instance - During cluster installation, the NVIDIA Run:ai cluster operator creates a dedicated Prometheus Custom Resource (CR) in the runai namespace. This step applies to both Kubernetes and OpenShift, and produces the Prometheus instance that NVIDIA Run:ai uses for monitoring.

Features

Each feature is configured under features in the configuration file, and only needs to be enabled for the workload types that tenant plans to run, with a mode field controlling how the installer handles it.

• install - Installs and configures the component for you.
• skip (default) - Does not install or check for the component. Use this when the component is already installed on the cluster, or when the feature does not apply to the workload types that tenant plans to run.

Each feature also accepts its own version field - omit it to install the latest version, or see Support Matrix for the supported version range if you want to set it explicitly.

Distributed Training

NVIDIA Run:ai supports distributed training - training AI models across multiple nodes - using the Kubeflow Training Operator for TensorFlow, PyTorch, XGBoost, and JAX workloads, and the MPI Operator for MPI v2-based workloads. Both are required for compatibility with advanced workload capabilities such as stopping a workload and scheduling rules.

The Kubeflow Training Operator ships with MPI v1 support built in, which conflicts with the separate MPI Operator (MPI v2). When both features.kubeflow.mode: install and features.mpiOperator.mode: install are set, the installer handles this automatically - disabling MPI v1 support and removing its CRD before installing the MPI Operator.

Standard Inference

NVIDIA Run:ai uses Knative Serving to enable standard (single-node) inference. Knative Serving lets a tenant serve AI models as production workloads, using NVIDIA NIM, vLLM, or custom inference servers. It provides serverless, dynamic autoscaling (including scale to zero when idle), rolling updates without disrupting served traffic, and endpoints reachable via external access (a wildcard FQDN) or internal, cluster-local access.

Setting features.knative.mode: install installs the Knative operator and applies a KnativeServing custom resource:

• Kubernetes - Installs Knative Serving via the Knative Operator, and configures scale-to-zero autoscaling, the wildcard inference domain, Kourier as the ingress class, and the Kubernetes pod-spec features NVIDIA Run:ai workloads require.
• OpenShift - Installs Knative Serving via the OpenShift Serverless operator (OperatorHub), and configures the wildcard inference domain and the Kubernetes pod-spec features NVIDIA Run:ai workloads require. External access goes through OpenShift Routes instead of Kourier, and scale-to-zero uses the operator’s default.

Autoscaling

NVIDIA Run:ai allows for autoscaling a deployment according to the metrics below:

• Latency (milliseconds)
• Throughput (requests/sec)
• Concurrency (requests)

Using a custom metric (for example, Latency) requires the Knative Horizontal Pod Autoscaler (HPA) extension. Setting features.knative.mode: install installs this extension automatically alongside Knative Serving, so no additional step is needed.

Distributed Inference

NVIDIA Run:ai uses LeaderWorkerSet (LWS) to enable distributed (multi-node) inference - serving large language models that do not fit on a single node by distributing them across multiple nodes.

Setting features.lws.mode: install installs LWS:

• Kubernetes - Installs the LWS Helm chart into the lws-system namespace.
• OpenShift - Installs the OpenShift LWS Operator (via OperatorHub) into the openshift-lws-operator namespace.

Either way, the installer automatically points NVIDIA Run:ai (clusterConfig.lws.namespace) at the namespace LWS runs in, so no manual configuration is needed for NVIDIA Run:ai to detect it.

NIM Operator

The NVIDIA NIM Operator enables Kubernetes cluster administrators to operate the software components and services necessary to deploy NVIDIA NIMs and NVIDIA NeMo microservices in Kubernetes. NVIDIA Run:ai provides out-of-the-box support for submitting NIM Operator workloads via YAML.

Grove

NVIDIA Run:ai uses NVIDIA Grove to orchestrate multi-component, gang-scheduled workloads, such as disaggregated NVIDIA Dynamo inference deployments where a single workload is split into multiple functions (for example, a router, prefill, and decode component), each made up of one or more leader-worker replica sets. Grove acts as the Kubernetes API (CRD) and controller for these pods, and is required alongside the Dynamo Operator for disaggregated Dynamo deployments.

Dynamo

The Dynamo Operator is a Kubernetes operator that simplifies the deployment, configuration, and lifecycle management of DynamoGraphs. NVIDIA Run:ai provides out-of-the-box support for submitting Dynamo workloads via YAML.