> This page is for Multi-Tenant, version v3.0 (default). > For other versions, use one of these documentation indexes: > - v3.0 (default): https://docs.nvidia.com/run-ai/multi-tenant/v3.0/llms.txt > - v2.26: https://docs.nvidia.com/run-ai/multi-tenant/v2.26/llms.txt > - v2.25: https://docs.nvidia.com/run-ai/multi-tenant/v2.25/llms.txt > - v2.24: https://docs.nvidia.com/run-ai/multi-tenant/v2.24/llms.txt > For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://docs.nvidia.com/run-ai/llms.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/run-ai/_mcp/server. # 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 installed for the workloads that tenant plans to run. This page describes how to install them manually using Helm, along with any Helm-specific handling. For detailed version compatibility, refer to the [Support Matrix](/run-ai/multi-tenant/documentation/installation-and-configuration/support-matrix). | Component | Type | Required for | |---|---|---| | GPU Operator | Prerequisite | Every tenant cluster | | Network Operator | Prerequisite | RDMA or Multi-Node NVLink (MNNVL)-capable nodes | | DRA Driver | Prerequisite | Multi-Node NVLink (e.g. GB200) | | Prometheus | Prerequisite | Every tenant cluster | | Kubeflow Training Operator | Feature | Distributed training | | MPI Operator | Feature | MPI-based distributed training | | Knative Serving | Feature | Standard (single-node) inference | | LeaderWorkerSet (LWS) | Feature | Distributed (multi-node) inference | | NIM Operator | Feature | NVIDIA Inference Microservices | | Grove | Feature | Multi-component, gang-scheduled workload orchestration (for example, disaggregated Dynamo deployments) | | Dynamo | Feature | Inference pipeline optimization | ## Before You Start Complete [Preparations](/run-ai/multi-tenant/documentation/installation-and-configuration/install/manual-helm/preparations) before starting here. The operators and frameworks described on this page must be installed before you [create the tenant and cluster](/run-ai/multi-tenant/documentation/installation-and-configuration/install/manual-helm/onboard-a-tenant/create-a-tenant-and-cluster), on that tenant's own Kubernetes cluster. ## Prerequisites ### NVIDIA GPU Operator The [NVIDIA GPU Operator](https://docs.nvidia.com/datacenter/cloud-native/gpu-operator/latest/index.html) provisions and manages GPUs on worker nodes, and is required for a tenant cluster to run AI workloads. **Note** For [Multi-Node NVLink](/run-ai/self-hosted/documentation/platform-management/manage-ai-initiatives/resources/using-gb200) support (e.g. GB200), GPU Operator 25.3 and above is required. **Note** To use GPU fractions (memory fractions, dynamic memory fractions, or compute modes), the following are required: * GPU Operator 26.7.1 or later is required. * CUDA driver r615 or later is required. GPU Operator 26.7.1 does not default to it, so set the driver version explicitly rather than relying on the default - with `--set driver.version=` on Kubernetes, or by setting `driver.version` in the `ClusterPolicy` spec on OpenShift, where the GPU Operator is installed from OperatorHub. * The NRI plugin must be disabled. Install the GPU Operator with `cdi.nriPluginEnabled` set to `false` (`--set cdi.nriPluginEnabled=false` on Kubernetes, or `cdi.nriPluginEnabled: false` in the `ClusterPolicy` spec on OpenShift). GPU memory fractions do not function while the NRI plugin is enabled. See [GPU Memory Fractions](/run-ai/self-hosted/documentation/platform-management/runai-scheduler/resource-optimization/gpu-fractions/memory-fractions#prerequisites) for the full requirement set. * For installation instructions, see [Installing the NVIDIA GPU Operator](https://docs.nvidia.com/datacenter/cloud-native/gpu-operator/latest/getting-started.html). * Use the default `gpu-operator` namespace. Otherwise, you must specify the target namespace using the flag `clusterConfig.global.nvidiaDcgmExporter.namespace` as described in [Advanced Cluster Configurations](/run-ai/self-hosted/documentation/installation-and-configuration/config/advanced-cluster-configuration). * NVIDIA drivers may already be installed on the nodes. In such cases, use the NVIDIA GPU Operator flags `--set driver.enabled=false`. [DGX OS](https://docs.nvidia.com/dgx/dgx-os-6-user-guide/release_notes.html) is one such example as it comes bundled with NVIDIA Drivers. * For distribution-specific requirements and additional instructions, see the sections below: **OpenShift Container Platform (OCP)** The Node Feature Discovery (NFD) Operator is a prerequisite for the NVIDIA GPU Operator in OpenShift. Install the NFD Operator using the Red Hat OperatorHub catalog in the OpenShift Container Platform web console. For more information, see [Installing the Node Feature Discovery (NFD) Operator](https://docs.nvidia.com/datacenter/cloud-native/openshift/latest/install-nfd.html). **Rancher Kubernetes Engine 2 (RKE2)** Before installing the GPU Operator, verify the [host OS requirements](https://docs.rke2.io/add-ons/gpu_operators?GPUoperator=v25.3.x#host-os-requirements) are met. Then, install the [operator](https://docs.rke2.io/add-ons/gpu_operators#operator-installation). When installing GPU Operator v25.3, update the Helm values file as follows: ```yaml 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](https://docs.nvidia.com/datacenter/cloud-native/gpu-operator/latest/troubleshooting.html). ### 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. The Network Operator must be installed and configured as follows: 1. Install the network operator as detailed in [Network Operator Deployment on Vanilla Kubernetes Cluster](https://docs.nvidia.com/networking/display/kubernetes2440/getting-started-kubernetes.html#network-operator-deployment-on-vanilla-kubernetes-cluster). 2. Configure SR-IOV InfiniBand support as detailed in [Network Operator Deployment with an SR-IOV InfiniBand Network](https://docs.nvidia.com/networking/display/kubernetes2440/getting-started-kubernetes.html#network-operator-deployment-with-an-sr-iov-infiniband-network). ### NVIDIA Dynamic Resource Allocation (DRA) Driver When deploying on clusters with Multi-Node NVLink (e.g. GB200), the [NVIDIA DRA driver](https://docs.nvidia.com/datacenter/cloud-native/gpu-operator/26.7/dra-intro-install.html) 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](/run-ai/multi-tenant/documentation/installation-and-configuration/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](https://docs.nvidia.com/datacenter/cloud-native/gpu-operator/26.7/dra-intro-install.html) and, on OpenShift, [Installing the NVIDIA GPU Operator with DRA on OpenShift](https://docs.nvidia.com/datacenter/cloud-native/openshift/26.7/dra-gpu-ocp.html). **Important** NVIDIA Run:ai limitations in `GPUCluster` mode: * GPU fractions are not supported. * NVIDIA Run:ai shows each node's GPU model, count, and memory only when GPU Feature Discovery is deployed separately. See [Deploying gpu-feature-discovery in standalone mode](https://github.com/NVIDIA/k8s-device-plugin#deploying-gpu-feature-discovery-in-standalone-mode). * Workloads that request `nvidia.com/gpu` require [extended-resource translation](/run-ai/self-hosted/documentation/platform-management/runai-scheduler/scheduling/concepts-and-principles#extended-resource-translation), which requires Kubernetes 1.37 or later. 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. For details on how to configure this value using Helm or `runaiconfig`, see [Advanced Cluster Configurations](/run-ai/self-hosted/documentation/installation-and-configuration/config/advanced-cluster-configuration). ### Prometheus **Note** Installing Prometheus applies to Kubernetes only. 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](https://docs.redhat.com/en/documentation/openshift_container_platform/4.18/html-single/monitoring/index#about-ocp-monitoring), so no install is required. * **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. For RKE2, see [Enable Monitoring](https://ranchermanager.docs.rancher.com/how-to-guides/advanced-user-guides/monitoring-alerting-guides/enable-monitoring) for instructions on installing Prometheus. The following example installs the community [Kube-Prometheus Stack](https://artifacthub.io/packages/helm/prometheus-community/kube-prometheus-stack) using [helm](https://helm.sh/): ```bash helm repo add prometheus-community https://prometheus-community.github.io/helm-charts helm repo update helm install prometheus prometheus-community/kube-prometheus-stack \ -n monitoring --create-namespace --set grafana.enabled=false ``` ## Features To enable NVIDIA Run:ai capabilities such as Distributed Training and Inference, additional Kubernetes applications (frameworks) must be installed on the tenant's cluster. Only install what that tenant needs for the workload types it plans to run. ### Distributed Training NVIDIA Run:ai supports distributed training - training AI models across multiple nodes - using the [Kubeflow Training Operator](https://www.kubeflow.org/docs/components/training/installation/) for [TensorFlow](https://www.tensorflow.org/), [PyTorch](https://pytorch.org/), [XGBoost](https://xgboost.readthedocs.io/), and [JAX](https://docs.jax.dev/en/latest/index.html) workloads, and the [MPI Operator](https://github.com/kubeflow/mpi-operator) for [MPI v2](https://docs.open-mpi.org/)-based workloads. Both are required for compatibility with advanced workload capabilities such as stopping a workload and scheduling rules. It is recommended to use **Kubeflow Training Operator v1.9.2**, and **MPI Operator v0.6.0 or later**. * To install the Kubeflow Training Operator for TensorFlow, PyTorch, XGBoost and JAX frameworks, run the following command: ```bash kubectl apply --server-side -k "github.com/kubeflow/training-operator.git/manifests/overlays/standalone?ref=v1.9.2" ``` * To install the MPI Operator for MPI v2, run the following command: ```bash kubectl apply --server-side -f https://raw.githubusercontent.com/kubeflow/mpi-operator/v0.6.0/deploy/v2beta1/mpi-operator.yaml ``` **Note** If you require both the MPI Operator and Kubeflow Training Operator, follow the steps below: * Install the Kubeflow Training Operator as described above. * Disable and delete MPI v1 in the Kubeflow Training Operator by running: ```bash kubectl patch deployment training-operator -n kubeflow --type='json' -p='[{"op": "add", "path": "/spec/template/spec/containers/0/args", "value": ["--enable-scheme=tfjob", "--enable-scheme=pytorchjob", "--enable-scheme=xgboostjob", "--enable-scheme=jaxjob"]}]' kubectl delete crd mpijobs.kubeflow.org ``` * Install the MPI Operator as described above. ### Standard Inference NVIDIA Run:ai uses [Knative Serving](https://knative.dev/docs/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. Supports Knative versions 1.19 to 1.21. **Note** On OpenShift, Knative is installed through the OpenShift Serverless operator, which supports Knative 1.17 only. To configure inference: * **Inference request metrics (OTLP)** - For Knative Serving 1.19 or later, add the `observability` block to your `KnativeServing` YAML to export request metrics to the NVIDIA Run:ai API and UI. See [Inference Request Metrics (OTLP)](#inference-request-metrics-otlp) below. * **Internal or external access** - Choose how inference services are reached: externally via a wildcard FQDN (the default) or cluster-local. See the matching section below. #### Inference Request Metrics (OTLP) To surface inference request metrics in the NVIDIA Run:ai API and UI, configure Knative Serving 1.19 or later to export request metrics over OpenTelemetry Protocol (OTLP) to the NVIDIA Run:ai Prometheus instance. The `observability` block included in the `KnativeServing` YAML examples below sets the required `config-observability` keys (`metrics-protocol`, `request-metrics-protocol`, `request-metrics-endpoint`). For supported fields, see [Enabling metric collection](https://knative.dev/docs/serving/observability/metrics/collecting-metrics/#enabling-metric-collection) in the Knative documentation. The OTLP receiver on the NVIDIA Run:ai Prometheus instance is enabled by default. To disable it, set `spec.prometheus.config.enableOTLPReceiver: false`. See [Advanced Cluster Configurations](/run-ai/self-hosted/documentation/installation-and-configuration/config/advanced-cluster-configuration) for more details. #### External-Access Inference With external access, inference services are reachable from outside the cluster. Use this mode when inference workloads must be reachable by clients outside the cluster. On Kubernetes, external access exposes inference services through a wildcard FQDN (for example, `*.runai-inference.tenant-a-corp.com`). This requires the wildcard DNS record (see [FQDN and DNS](/run-ai/multi-tenant/documentation/installation-and-configuration/system-requirements/networking-setup#fqdn-and-dns)), the standard inference wildcard certificate (see [Preparations](/run-ai/multi-tenant/documentation/installation-and-configuration/install/manual-helm/preparations#standard-inference-wildcard-certificate)), and an HAProxy ingress to handle TLS termination. Follow the [Installing Knative](https://knative.dev/v1.18-docs/install/operator/knative-with-operators/) instructions or run: ```bash helm repo add knative-operator https://knative.github.io/operator helm install knative-operator --create-namespace --namespace knativeoperator --version 1.18.2 knative-operator/knative-operator ``` Once installed, follow the below steps: 1. Create the `knative-serving` namespace: ```bash kubectl create ns knative-serving ``` 2. Create a YAML file named `knative-serving.yaml` and replace the placeholder FQDN with your wildcard inference domain (for example, `runai-inference.tenant-a-corp.com`). ```yaml apiVersion: operator.knative.dev/v1beta1 kind: KnativeServing metadata: name: knative-serving namespace: knative-serving spec: config: config-autoscaler: enable-scale-to-zero: "true" config-features: kubernetes.podspec-affinity: enabled kubernetes.podspec-init-containers: enabled kubernetes.podspec-persistent-volume-claim: enabled kubernetes.podspec-persistent-volume-write: enabled kubernetes.podspec-schedulername: enabled kubernetes.podspec-securitycontext: enabled kubernetes.podspec-tolerations: enabled kubernetes.podspec-volumes-emptydir: enabled kubernetes.podspec-fieldref: enabled kubernetes.containerspec-addcapabilities: enabled kubernetes.podspec-nodeselector: enabled multi-container: enabled kubernetes.podspec-hostipc: enabled kubernetes.podspec-hostnetwork: enabled domain: runai-inference.tenant-a-corp.com: "" # replace with the wildcard FQDN for Inference network: domainTemplate: '{{.Name}}-{{.Namespace}}.{{.Domain}}' ingress-class: kourier.ingress.networking.knative.dev default-external-scheme: https observability: metrics-protocol: "prometheus" request-metrics-protocol: "http/protobuf" request-metrics-endpoint: "http://prometheus-operated.runai.svc.cluster.local:9090/api/v1/otlp/v1/metrics" high-availability: replicas: 2 ingress: kourier: enabled: true ``` 3. Apply the changes: ```bash kubectl apply -f knative-serving.yaml ``` 4. Configure HAProxy to proxy requests to Kourier / Knative and handle TLS termination using the wildcard certificate. Create a YAML file named `knative-ingress.yaml` and replace the FQDN placeholders with your wildcard inference domain: ```yaml apiVersion: networking.k8s.io/v1 kind: Ingress metadata: name: knative-serving namespace: knative-serving spec: ingressClassName: haproxy rules: - host: '*.runai-inference.tenant-a-corp.com' # replace with the wildcard FQDN for Inference http: paths: - backend: service: name: kourier port: number: 80 path: / pathType: Prefix tls: - hosts: - '*.runai-inference.tenant-a-corp.com' # replace with the wildcard FQDN for Inference secretName: runai-cluster-inference-tls-secret ``` 5. Apply the changes: ```bash kubectl apply -f knative-ingress.yaml ``` On OpenShift Serverless, external access is the platform default: each KnativeService automatically gets an OpenShift Route. Follow the [Installing the OpenShift Serverless Operator](https://docs.redhat.com/en/documentation/red_hat_openshift_serverless/1.37/html/installing_openshift_serverless/install-serverless-operator) instructions. Once installed, follow the steps below: 1. Create the `knative-serving` project: ```bash oc new-project knative-serving ``` 2. Create a YAML file named `knative-serving.yaml`: ```yaml apiVersion: operator.knative.dev/v1beta1 kind: KnativeServing metadata: finalizers: - knative-serving-openshift - knativeservings.operator.knative.dev name: knative-serving namespace: knative-serving spec: config: config-features: kubernetes.podspec-tolerations: enabled kubernetes.podspec-volumes-emptydir: enabled kubernetes.podspec-persistent-volume-claim: enabled multi-container: enabled kubernetes.podspec-persistent-volume-write: enabled kubernetes.podspec-fieldref: enabled kubernetes.podspec-schedulername: enabled kubernetes.podspec-nodeselector: enabled kubernetes.podspec-init-containers: enabled kubernetes.podspec-securitycontext: enabled kubernetes.podspec-affinity: enabled kubernetes.containerspec-addcapabilities: enabled observability: metrics-protocol: "prometheus" request-metrics-protocol: "http/protobuf" request-metrics-endpoint: "http://prometheus-operated.runai.svc.cluster.local:9090/api/v1/otlp/v1/metrics" controller-custom-certs: name: '' type: '' registry: {} ``` 3. Apply the changes: ```bash oc apply -f knative-serving.yaml ``` #### Cluster-Local Inference With cluster-local access, inference services are reachable only from inside the cluster through the cluster-internal domain (`..svc.cluster.local`). This mode is recommended when inference workloads are consumed by other applications running in the same cluster. On OpenShift Serverless, external access is the platform default; cluster-local must be enabled per inference workload. See the OpenShift tab below. On Kubernetes, cluster-local access does not require an external DNS record, a wildcard certificate, or an HAProxy ingress because traffic does not leave the cluster. Follow the [Installing Knative](https://knative.dev/docs/install/operator/knative-with-operators/) instructions or run: ```bash helm repo add knative-operator https://knative.github.io/operator helm install knative-operator --create-namespace --namespace knativeoperator --version {VERSION} knative-operator/knative-operator ``` Once installed, follow the steps below: 1. Create the `knative-serving` namespace: ```bash kubectl create ns knative-serving ``` 2. Create a YAML file named `knative-serving.yaml`: ```yaml apiVersion: operator.knative.dev/v1beta1 kind: KnativeServing metadata: name: knative-serving namespace: knative-serving spec: config: config-autoscaler: enable-scale-to-zero: "true" config-features: kubernetes.podspec-affinity: enabled kubernetes.podspec-init-containers: enabled kubernetes.podspec-persistent-volume-claim: enabled kubernetes.podspec-persistent-volume-write: enabled kubernetes.podspec-schedulername: enabled kubernetes.podspec-securitycontext: enabled kubernetes.podspec-tolerations: enabled kubernetes.podspec-volumes-emptydir: enabled kubernetes.podspec-fieldref: enabled kubernetes.containerspec-addcapabilities: enabled kubernetes.podspec-nodeselector: enabled multi-container: enabled kubernetes.podspec-hostipc: enabled kubernetes.podspec-hostnetwork: enabled domain: svc.cluster.local: "" network: domainTemplate: '{{.Name}}.{{.Namespace}}.{{.Domain}}' ingress-class: kourier.ingress.networking.knative.dev default-external-scheme: http observability: metrics-protocol: "prometheus" request-metrics-protocol: "http/protobuf" request-metrics-endpoint: "http://prometheus-operated.runai.svc.cluster.local:9090/api/v1/otlp/v1/metrics" high-availability: replicas: 2 ingress: kourier: enabled: true service-type: ClusterIP ``` 3. Apply the changes: ```bash kubectl apply -f knative-serving.yaml ``` On OpenShift Serverless, external access is the platform default: KnativeServices automatically get an OpenShift Route. To make an inference workload cluster-local, configure the workload's routing settings so that the `networking.knative.dev/visibility=cluster-local` label is applied to the underlying KnativeService. Follow the [Installing the OpenShift Serverless Operator](https://docs.redhat.com/en/documentation/red_hat_openshift_serverless/1.37/html/installing_openshift_serverless/install-serverless-operator) instructions. Once installed, follow the steps below: 1. Create the `knative-serving` project: ```bash oc new-project knative-serving ``` 2. Create a YAML file named `knative-serving.yaml`. ```yaml apiVersion: operator.knative.dev/v1beta1 kind: KnativeServing metadata: finalizers: - knative-serving-openshift - knativeservings.operator.knative.dev name: knative-serving namespace: knative-serving spec: config: config-features: kubernetes.podspec-tolerations: enabled kubernetes.podspec-volumes-emptydir: enabled kubernetes.podspec-persistent-volume-claim: enabled multi-container: enabled kubernetes.podspec-persistent-volume-write: enabled kubernetes.podspec-fieldref: enabled kubernetes.podspec-schedulername: enabled kubernetes.podspec-nodeselector: enabled kubernetes.podspec-init-containers: enabled kubernetes.podspec-securitycontext: enabled kubernetes.podspec-affinity: enabled kubernetes.containerspec-addcapabilities: enabled observability: metrics-protocol: "prometheus" request-metrics-protocol: "http/protobuf" request-metrics-endpoint: "http://prometheus-operated.runai.svc.cluster.local:9090/api/v1/otlp/v1/metrics" controller-custom-certs: name: '' type: '' registry: {} ``` 3. Apply the changes: ```bash oc apply -f knative-serving.yaml ``` #### Autoscaling NVIDIA Run:ai allows for autoscaling a deployment according to the below metrics: * Latency (milliseconds) * Throughput (requests/sec) * Concurrency (requests) Using a custom metric (for example, Latency) requires installing the [Kubernetes Horizontal Pod Autoscaler (HPA)](https://knative.dev/docs/install/yaml-install/serving/install-serving-with-yaml/#install-optional-serving-extensions). Use the following command to install. Make sure to update the `{VERSION}` in the below command with a supported Knative version. ```bash kubectl apply -f https://github.com/knative/serving/releases/download/knative-{VERSION}/serving-hpa.yaml ``` ### Distributed Inference NVIDIA Run:ai supports distributed inference (multi-node) deployments using the Leader Worker Set (LWS). To enable this capability, you must install the [LWS Helm chart](https://lws.sigs.k8s.io/docs/installation/#install-by-helm) in version 0.7.0 or higher on the tenant's cluster: ```bash CHART_VERSION=0.7.0 helm install lws oci://registry.k8s.io/lws/charts/lws \ --version=$CHART_VERSION \ --namespace lws-system \ --create-namespace \ --wait --timeout 300s ``` ### NIM Operator The [NVIDIA NIM Operator](https://docs.nvidia.com/nim-operator/latest/) 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](/run-ai/self-hosted/documentation/workloads-in-nvidia-run-ai/submit-via-yaml). ### Grove NVIDIA Run:ai uses [NVIDIA Grove](https://developer.nvidia.com/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](https://docs.nvidia.com/dynamo) 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](/run-ai/self-hosted/documentation/workloads-in-nvidia-run-ai/submit-via-yaml). ## Next Steps With the tenant cluster's environment prepared, continue to [Create a Tenant and Cluster](/run-ai/multi-tenant/documentation/installation-and-configuration/install/manual-helm/onboard-a-tenant/create-a-tenant-and-cluster) to register and install the cluster.