Add Rafay to partner validated docs

partner-validated/index.rst

@@ -30,6 +30,7 @@ About Partner-Validated Configurations
    k0rdent.rst
    mirantis-mke.rst
    suse-rke2.rst
+   rafay.rst
    NVIDIA GPU Operator Documentation <https://docs.nvidia.com/datacenter/cloud-native/gpu-operator/latest/index.html>
 
 Partner-validated configurations help end users who want to use

partner-validated/rafay.rst

@@ -0,0 +1,306 @@
+.. license-header
+  SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+  SPDX-License-Identifier: Apache-2.0
+
+  Licensed under the Apache License, Version 2.0 (the "License");
+  you may not use this file except in compliance with the License.
+  You may obtain a copy of the License at
+
+  http://www.apache.org/licenses/LICENSE-2.0
+
+  Unless required by applicable law or agreed to in writing, software
+  distributed under the License is distributed on an "AS IS" BASIS,
+  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  See the License for the specific language governing permissions and
+  limitations under the License.
+
+.. headings # #, * *, =, -, ^, "
+
+
+
+.. |prod-name-long| replace:: Rafay Platform
+.. |prod-name-short| replace:: Rafay
+
+#############################################
+|prod-name-long| with the NVIDIA GPU Operator
+#############################################
+
+
+********************************************
+About |prod-name-long| with the GPU Operator
+********************************************
+
+The |prod-name-long| provides the software foundation for operating and governing modern AI
+infrastructure, including bare-metal provisioning, infrastructure lifecycle management, Kubernetes,
+virtual machines, AI workbenches, and token factories. Together these capabilities help operators
+increase infrastructure utilization and monetize AI services. Platform capabilities include
+centralized fleet lifecycle management, cluster blueprints and Add-On management, zero-trust kubectl
+access, RBAC/SSO, per-tenant cost allocation, and centralized audit logging.
+
+The |prod-name-long| includes a Managed Kubernetes Service (Rafay MKS), a CNCF-conformant Kubernetes
+distribution that provisions and manages the full lifecycle of upstream Kubernetes clusters on bare
+metal and virtual machines. Using the NVIDIA GPU Operator with Rafay MKS gives customers a validated,
+repeatable way to deliver GPU-accelerated Kubernetes infrastructure for AI/ML workloads:
+
+* **Standardized GPU cluster provisioning.** Platform teams can package the GPU Operator as an Add-On
+  in a Rafay cluster blueprint so every MKS cluster comes up with a consistent, version-pinned NVIDIA
+  software stack (driver, container toolkit, device plugin, DCGM, and so on).
+
+* **Fleet-wide lifecycle management.** The Rafay controller manages Kubernetes, CNI, CSI, and Add-On
+  lifecycle across many clusters, which simplifies keeping GPU Operator versions, Kubernetes versions,
+  and container runtimes in a known-good, validated combination.
+
+* **Secure day-2 operations.** Operators can validate GPU health remotely (for example, running
+  ``nvidia-smi`` in a GPU Operator pod through Rafay's zero-trust kubectl) without bastion hosts or
+  shared kubeconfigs.
+
+* **Multi-tenant, self-service GPU consumption.** On top of MKS, the |prod-name-long| lets platform
+  teams publish Jupyter Notebooks, LLM inference endpoints, AI/ML jobs, and custom services as SKUs in
+  a tenant-facing catalog. End users provision them on demand via a self-service portal, with RBAC,
+  quotas, and per-tenant metering and cost allocation applied automatically. Rafay's **Token Factory**
+  extends the model to token-metered LLM endpoints for delivering GenAI-as-a-service, and the **App
+  Marketplace** supports packaged applications including NVIDIA NIM microservices.
+
+* **Full-stack automation.** The same |prod-name-long| that manages the Rafay MKS distribution-based
+  Kubernetes cluster also automates the provisioning of bare-metal servers and VMs the cluster sits
+  on and manages the wider data-center inventory: virtual machines, storage, network switches, and
+  InfiniBand fabrics. From one control plane, GPU capacity can serve both K8s tenants and non-K8s
+  tenants (HPC jobs on SLURM, VM-based training pipelines) without a second orchestration layer.
+
+For more information about Rafay MKS, refer to the `Rafay product documentation
+<https://docs.rafay.co/learn/mks/>`__.
+
+
+******************************
+Validated Configuration Matrix
+******************************
+
+Rafay self-validated the NVIDIA GPU Operator with the following configuration:
+
+.. list-table::
+   :header-rows: 1
+
+   * - Product Name
+     - | NVIDIA
+       | GPU
+       | Operator
+     - | Operating
+       | System
+     - | Container
+       | Runtime
+     - | Kubernetes
+       | Version
+     - | Helm
+       | Version
+     - | NVIDIA
+       | GPU Model
+     - | NVIDIA
+       | GPU Driver
+     - Hardware Model
+     - Date Validated
+
+   * - Rafay MKS 3.1
+     - v26.3.1
+     - Ubuntu 24.04.3 LTS
+     - containerd 2.0.4
+     - v1.34.3
+     - v3.14.4
+     - NVIDIA H100 NVL
+     - 580.159.04
+     - | Supermicro SYS-221GE-NR
+       | Intel Xeon Gold 6548Y+ (32 cores/socket, 2 sockets)
+       | 1 TB RAM
+     - June 2026
+
+
+*************
+Prerequisites
+*************
+
+Before installing the NVIDIA GPU Operator on a Rafay MKS cluster, verify the following:
+
+* A running Rafay MKS cluster on Kubernetes v1.34.3 (or a Kubernetes version listed in the validated
+  configuration matrix), provisioned via the Rafay Console, Rafay CLI, API, or Terraform Provider.
+  Refer to the `Rafay MKS provisioning documentation
+  <https://docs.rafay.co/clusters/upstream/baremetal_vm/overview/>`__.
+
+* At least one worker node with a supported NVIDIA GPU (validated with NVIDIA H100 NVL) physically
+  installed.
+
+* Nodes running Ubuntu 24.04 LTS with containerd 2.0.x as the container runtime.
+
+* Cluster admin access via a kubeconfig downloaded from the Rafay Console, or zero-trust kubectl
+  access through the |prod-name-long|.
+
+* Helm v3 installed on the workstation used to install the GPU Operator Certification Helm chart.
+
+* Outbound access from the cluster to the NVIDIA NGC registry (nvcr.io) and the NVIDIA Helm
+  repository (https://helm.ngc.nvidia.com/nvidia).
+
+Verify readiness with the following commands.
+
+Confirm all nodes are ``Ready`` and report the expected Kubernetes version and container runtime:
+
+.. code-block:: console
+
+   $ kubectl get nodes -o wide
+
+*Example output:*
+
+.. code-block:: text
+
+   NAME         STATUS   ROLES           AGE   VERSION   ...   CONTAINER-RUNTIME
+   mks-cp-01    Ready    control-plane   7d    v1.34.3   ...   containerd://2.0.4
+   mks-gpu-01   Ready    worker          7d    v1.34.3   ...   containerd://2.0.4
+
+Confirm the NVIDIA GPU is visible on the worker node:
+
+.. code-block:: console
+
+   $ lspci | grep -i nvidia
+
+*Example output:*
+
+.. code-block:: text
+
+   17:00.0 3D controller: NVIDIA Corporation H100 NVL (rev a1)
+
+Confirm Helm 3.x is installed on the workstation:
+
+.. code-block:: console
+
+   $ helm version --short
+
+
+*******************************************
+Configuring Rafay MKS with the GPU Operator
+*******************************************
+
+On Rafay MKS, the NVIDIA GPU Operator is installed as an `Add-On <https://docs.rafay.co/blueprints/addons/>`_ (a versioned Helm chart managed by
+the Rafay controller) that is part of a Rafay Cluster Blueprint. This is the recommended fleet-native
+approach and lets every GPU-enabled MKS cluster in the fleet install the same NVIDIA stack from a
+single source of truth in the |prod-name-long|.
+
+Provisioning the MKS cluster
+============================
+
+Provision the Rafay MKS cluster with at least one GPU-enabled worker node using the Rafay Console,
+RCTL CLI, API, or Terraform. Refer to the `Rafay MKS documentation for bare metal and VMs
+<https://docs.rafay.co/clusters/upstream/baremetal_vm/overview/>`__.
+
+Download the kubeconfig from the Rafay Console and confirm cluster access:
+
+.. code-block:: console
+
+   $ kubectl get nodes
+
+*Example output:*
+
+.. code-block:: text
+
+   NAME         STATUS   ROLES           AGE   VERSION
+   mks-cp-01    Ready    control-plane   7d    v1.34.3
+   mks-gpu-01   Ready    worker          7d    v1.34.3
+
+Preparing the cluster for the GPU Operator
+==========================================
+
+Because Rafay MKS provisions upstream Kubernetes, no MKS-specific privilege or configuration changes
+are expected to be required before installing the GPU Operator.
+
+Installing the GPU Operator as a Rafay Add-On
+=============================================
+
+Install the GPU Operator by defining it as a Rafay Add-On and referencing that Add-On from a Cluster
+Blueprint applied to your GPU-enabled MKS clusters.
+
+#. In the Rafay Console, create a new Add-On for the NVIDIA GPU Operator with the following
+   configuration:
+
+   * Source: https://helm.ngc.nvidia.com/nvidia
+
+   * Version: v26.3.1
+
+   * Namespace: ``gpu-operator`` (created by the Add-On if it does not exist).
+
+   * Values: the certification pinned the NVIDIA driver version explicitly. Here is the
+     ``values.yaml`` for the Add-On:
+
+     .. code-block:: yaml
+
+        driver:
+          enabled: true
+          repository: nvcr.io/nvidia
+          image: driver
+          version: "580.159.04"
+
+#. Reference the GPU Operator Add-On from a **Cluster Blueprint** in the same Rafay Project. The
+   Blueprint can include other Add-Ons the target clusters require (CNI, storage, observability, and
+   so on).
+
+#. Deploy the Cluster Blueprint to the target GPU-enabled MKS cluster(s). The Rafay control plane
+   reconciles the cluster to the Blueprint and installs the GPU Operator via Helm.
+
+#. Wait for all GPU Operator pods to reach ``Running`` or ``Completed``:
+
+   .. code-block:: console
+
+      $ kubectl get pods -n gpu-operator
+
+
+*****************************************
+Verifying Rafay MKS with the GPU Operator
+*****************************************
+
+View the nodes and the number of NVIDIA GPUs on each node:
+
+.. code-block:: console
+
+   $ kubectl get nodes \
+       -o=custom-columns=NAME:.metadata.name,GPUs:.metadata.labels.nvidia\.com/gpu\.count
+
+*Example output:*
+
+.. code-block:: text
+
+   NAME         GPUs
+   mks-gpu-01   1
+
+Run ``nvidia-smi`` through the driver daemonset. Rafay customers can run this command from the Rafay
+Console using the zero-trust kubectl web shell:
+
+.. code-block:: console
+
+   $ kubectl exec -n gpu-operator -it \
+       $(kubectl get pods -n gpu-operator -l app=nvidia-driver-daemonset -o name | head -1) \
+       -- nvidia-smi
+
+The output shows the NVIDIA H100 NVL GPU, the driver version, and the CUDA version.
+
+Refer to `Running Sample GPU Applications
+<https://docs.nvidia.com/datacenter/cloud-native/gpu-operator/latest/getting-started.html#running-sample-gpu-applications>`__
+in the NVIDIA GPU Operator documentation for additional verification workloads.
+
+
+***************
+Getting Support
+***************
+
+* Rafay product documentation: https://docs.rafay.co
+
+* Rafay product support: support@rafay.co
+
+
+*******************
+Related Information
+*******************
+
+* Rafay MKS documentation: https://docs.rafay.co/learn/mks/
+
+* Rafay MKS on bare metal and VMs: https://docs.rafay.co/clusters/upstream/baremetal_vm/overview/
+
+* NVIDIA GPU Operator documentation:
+  https://docs.nvidia.com/datacenter/cloud-native/gpu-operator/latest/index.html
+
+* NVIDIA GPU Operator platform support:
+  https://docs.nvidia.com/datacenter/cloud-native/gpu-operator/latest/platform-support.html