OpenNebula · rsmontero · Sep 8, 2026 · Sep 8, 2026 · Sep 8, 2026
diff --git a/content/getting_started/understand_opennebula/opennebula_concepts/screencasts.md b/content/getting_started/understand_opennebula/opennebula_concepts/screencasts.md
@@ -63,7 +63,7 @@ screencast updates.
 
 <div class="screencast-grid">
 {{< screencast id="cLvmSUUZh1Y" title="OpenNebula Frontend High Availability" thumbnail="frontend_ha.png">}}
-{{< screencast id="ea8nGH4bvWQ" title="Virtual Machine High Availability in OpenNebula" thumbnail="vm_ha.png">}}
+{{< screencast id="IFRuZV8tuhA" title="Virtual Machine High Availability in OpenNebula" thumbnail="vm_ha.png">}}
 {{< screencast id="HuHqGAwNzW8" title="Optimizing Infrastructure at Scale with OpenNebula’s AI-Powered DRS" thumbnail="optimizing_infra_ha.png" >}}
 </div>
 

diff --git a/content/platform_services/slurm/overview.md b/content/platform_services/slurm/overview.md
@@ -5,10 +5,6 @@ weight: 2
 type: docs
 ---
 
-{{< alert title="Work In Progress" type="primary" >}}
-This Elastic Slurm appliance and documentation are currently under development. Please contact the [OpenNebula sales and customer support team](https://opennebula.io/contact/) if you would like to arrange a demonstration of OpenNebula's Slurm integration.
-{{< /alert >}} 
-
 The OpenNebula Elastic Slurm Service enables the deployment of scalable Slurm Clusters using preconfigured OpenNebula appliances and OneFlow.
 
 Slurm is an open source, fault-tolerant, and highly scalable workload manager and job scheduling system for executing AI and HPC workloads on Linux Clusters. It allocates compute resources to workloads, starts and monitors jobs on the assigned nodes, and manages pending jobs through scheduling queues. Slurm operates without kernel modifications and is largely self-contained.
@@ -23,4 +19,4 @@ The appliances are designed to operate together as roles within a OneFlow servic
 
 This architecture allows worker capacity to be added or removed through OneFlow, providing a foundation for elastic Slurm Clusters managed through OpenNebula.
 
-For deployment instructions, configuration options, and operational details, refer to the [Slurm Wiki Documentation](https://github.com/OpenNebula/one-apps/wiki/slurm_intro).
+For deployment instructions, configuration options, and operational details, refer to the [Slurm Wiki Documentation](https://github.com/OpenNebula/one-apps/wiki/slurm_intro). There is also a [OneSlurm Tutorial]({{% relref "solutions/ai_factory_blueprints/direct_ai_execution/nvidia_slurm/" %}}) in the AI Factory Blueprints.
diff --git a/.../product/cluster_configuration/pci_passthrough_sriov/axelera_gpu_passthrough.md b/.../product/cluster_configuration/pci_passthrough_sriov/axelera_gpu_passthrough.md
@@ -9,8 +9,6 @@ tags: ['AI','NVIDIA']
 weight: "9"
 ---
 
-{{< alert title="Work In Progress" type="primary" >}} GPU passthrough functionality for Axelera GPUs is currently under active development. If you would like to discuss a demonstration, please contact the [OpenNebula sales and customer support team](https://opennebula.io/contact/).{{< /alert >}}
-
 ## Overview
 
 This guide describes how to assign an Axelera Metis AI Processing Unit (AIPU) directly to an OpenNebula Virtual Machine using PCI passthrough. The device is exclusively owned by the guest while the Virtual Machine is running; the Axelera driver and Voyager SDK are therefore installed in the guest, not on the Host.

diff --git a/...nt/product/cluster_configuration/pci_passthrough_sriov/nvidia_grace_platform.md b/...nt/product/cluster_configuration/pci_passthrough_sriov/nvidia_grace_platform.md
@@ -9,7 +9,6 @@ tags: ['AI','NVIDIA']
 weight: "7"
 ---
 
-
 {{< alert title="NVIDIA-Certified Hypervisor" color="primary" >}}
 OpenNebula is an [**NVIDIA-Certified Hypervisor**](https://docs.nvidia.com/certification-programs/certified-hypervisors/latest/nvidia-certified-hypervisors.html) for the Grace Blackwell platform. This certification validates that OpenNebula correctly exposes NVIDIA GPUs and associated PCIe resources to Virtual Machines while preserving the topology, high-performance data paths, and near bare-metal performance required for accelerated AI and compute workloads. For users deploying NVIDIA Grace Blackwell infrastructure, this ensures that OpenNebula has been tested against NVIDIA-defined requirements for running GPU-accelerated workloads in virtualized environments.{{< /alert >}}
 

diff --git a/...t/product/cluster_configuration/pci_passthrough_sriov/nvidia_mig_passthrough.md b/...t/product/cluster_configuration/pci_passthrough_sriov/nvidia_mig_passthrough.md
@@ -9,9 +9,7 @@ tags: ['AI','NVIDIA']
 weight: "6"
 ---
 
-## NVIDIA vGPU and MIG-backed vGPU
-
-### Overview
+## Overview
 
 NVIDIA vGPU enables multiple Virtual Machines to share the resources of a physical GPU by exposing one or more virtual GPU (vGPU) profiles that can be independently assigned to guest operating systems.
 

diff --git a/content/product/cluster_configuration/pci_passthrough_sriov/overview.md b/content/product/cluster_configuration/pci_passthrough_sriov/overview.md
@@ -68,10 +68,10 @@ The PCI passthrough documentation is organized into the following guides:
 | [Network Interfaces]({{% relref "product/cluster_configuration/pci_passthrough_sriov/network_interfaces/" %}}) | Configure PCI passthrough and SR-IOV for network adapters. |
 | [NVIDIA GPU Passthrough]({{% relref "product/cluster_configuration/pci_passthrough_sriov/nvidia_gpu_passthrough/" %}}) | Configure NVIDIA GPUs for passthrough. | 
 | [NVIDIA vGPU and MIG-backed GPU]({{% relref "product/cluster_configuration/pci_passthrough_sriov/nvidia_mig_passthrough/" %}}) | Configure NVIDIA GPUs for passthrough with mutli-VM resource sharing through vGPU and MIG. |
-| [NVIDIA Grace Platforms]({{% relref "product/cluster_configuration/pci_passthrough_sriov/nvidia_grace_platform/" %}}) | Configure NVIDIA GPUs for passthrough on Grace Hopper and Grace Blackwell platforms. |
+| [NVIDIA Grace Platform Virtualization (AI)]({{% relref "product/cluster_configuration/pci_passthrough_sriov/nvidia_grace_platform/" %}}) | Configure NVIDIA GPUs for passthrough on Grace Hopper and Grace Blackwell platforms. |
 | [AMD GPU Passthrough]({{% relref "product/cluster_configuration/pci_passthrough_sriov/amd_gpu_passthrough/" %}}) | Configure AMD GPUs for passthrough. | 
 | [Axelera GPU Passthrough]({{% relref "product/cluster_configuration/pci_passthrough_sriov/axelera_gpu_passthrough/" %}}) | Configure Axelera GPUs for passthrough. | 
-| [NVIDIA Fabric Manager]({{% relref "product/cluster_configuration/pci_passthrough_sriov/one_fabricmanager/" %}}) | Configure NVIDIA Fabric Manager for supported GPU platforms. |
+| [NVIDIA Fabric Manager (AI)]({{% relref "product/cluster_configuration/pci_passthrough_sriov/one_fabricmanager/" %}}) | Configure NVIDIA Fabric Manager for supported GPU platforms. |
 
 ## Next Steps
-Original file line number
+Diff line change
@@ Expand Up / @@ -9,7 +9,6 @@ tags: ['AI','NVIDIA'] @@
     weight: "7"
     ---
     {{< alert title="NVIDIA-Certified Hypervisor" color="primary" >}}
     OpenNebula is an [**NVIDIA-Certified Hypervisor**](https://docs.nvidia.com/certification-programs/certified-hypervisors/latest/nvidia-certified-hypervisors.html) for the Grace Blackwell platform. This certification validates that OpenNebula correctly exposes NVIDIA GPUs and associated PCIe resources to Virtual Machines while preserving the topology, high-performance data paths, and near bare-metal performance required for accelerated AI and compute workloads. For users deploying NVIDIA Grace Blackwell infrastructure, this ensures that OpenNebula has been tested against NVIDIA-defined requirements for running GPU-accelerated workloads in virtualized environments.{{< /alert >}}
@@ Expand Down @@