From d18abeba92fbb12c6d447b0bd209b5a2bafba7a3 Mon Sep 17 00:00:00 2001 From: Abigail McCarthy <20771501+a-mccarthy@users.noreply.github.com> Date: Thu, 16 Jul 2026 17:35:14 -0400 Subject: [PATCH 1/3] Add Rafay to partner validated docs Signed-off-by: Abigail McCarthy <20771501+a-mccarthy@users.noreply.github.com> --- partner-validated/index.rst | 1 + partner-validated/rafay.rst | 304 ++++++++++++++++++++++++++++++++++++ 2 files changed, 305 insertions(+) create mode 100644 partner-validated/rafay.rst diff --git a/partner-validated/index.rst b/partner-validated/index.rst index ddfffe7d9..3df64a8ac 100644 --- a/partner-validated/index.rst +++ b/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 Partner-validated configurations help end users who want to use diff --git a/partner-validated/rafay.rst b/partner-validated/rafay.rst new file mode 100644 index 000000000..b484ff2b3 --- /dev/null +++ b/partner-validated/rafay.rst @@ -0,0 +1,304 @@ +.. 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 +`__. + + +****************************** +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 + + * - 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 + + +************* +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 + `__. + +* 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 (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 +`__. + +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 +`__ +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 From 2f587737304fb95494a7d4687964954744b18d03 Mon Sep 17 00:00:00 2001 From: Abigail McCarthy <20771501+a-mccarthy@users.noreply.github.com> Date: Mon, 20 Jul 2026 15:56:31 -0400 Subject: [PATCH 2/3] Minor updates from review Signed-off-by: Abigail McCarthy <20771501+a-mccarthy@users.noreply.github.com> --- partner-validated/rafay.rst | 20 +++++++++++--------- 1 file changed, 11 insertions(+), 9 deletions(-) diff --git a/partner-validated/rafay.rst b/partner-validated/rafay.rst index b484ff2b3..c2b0f1873 100644 --- a/partner-validated/rafay.rst +++ b/partner-validated/rafay.rst @@ -80,23 +80,24 @@ Rafay self-validated the NVIDIA GPU Operator with the following configuration: .. list-table:: :header-rows: 1 - * - Product name + * - Product Name - | NVIDIA | GPU | Operator - | Operating - | system + | System - | Container - | runtime + | Runtime - | Kubernetes - | version + | Version - | Helm - | version + | Version - | NVIDIA - | GPU model + | GPU Model - | NVIDIA - | GPU driver - - Hardware model + | GPU Driver + - Hardware Model + - Date Validated * - Rafay MKS 3.1 - v26.3.1 @@ -109,6 +110,7 @@ Rafay self-validated the NVIDIA GPU Operator with the following configuration: - | Supermicro SYS-221GE-NR | Intel Xeon Gold 6548Y+ (32 cores/socket, 2 sockets) | 1 TB RAM + - June 2026 ************* @@ -174,7 +176,7 @@ Confirm Helm 3.x is installed on the workstation: Configuring Rafay MKS with the GPU Operator ******************************************* -On Rafay MKS, the NVIDIA GPU Operator is installed as an add-on (a versioned Helm chart managed by +On Rafay MKS, the NVIDIA GPU Operator is installed as an `Add-On `_ (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|. From 13d9175879cdc71501aa441876c0a7f9f00bc53b Mon Sep 17 00:00:00 2001 From: Abigail McCarthy <20771501+a-mccarthy@users.noreply.github.com> Date: Mon, 20 Jul 2026 16:03:11 -0400 Subject: [PATCH 3/3] updates to Add-On Signed-off-by: Abigail McCarthy <20771501+a-mccarthy@users.noreply.github.com> --- partner-validated/rafay.rst | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/partner-validated/rafay.rst b/partner-validated/rafay.rst index c2b0f1873..f9a1a5ad6 100644 --- a/partner-validated/rafay.rst +++ b/partner-validated/rafay.rst @@ -34,7 +34,7 @@ The |prod-name-long| provides the software foundation for operating and governin 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 +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 @@ -42,11 +42,11 @@ distribution that provisions and manages the full lifecycle of upstream Kubernet 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 +* **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 +* **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. @@ -235,7 +235,7 @@ Blueprint applied to your GPU-enabled MKS clusters. 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 + 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