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‎AKS-Arc/concepts-storage.md

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# Storage options for applications in AKS enabled by Azure Arc
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[!INCLUDE [hci-applies-to-23h2](includes/hci-applies-to-23h2.md)]
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[!INCLUDE [applies-to-azure stack-hci-and-windows-server-skus](includes/aks-hci-applies-to-skus/aks-hybrid-applies-to-azure-stack-hci-windows-server-sku.md)]
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Applications that run in AKS deployments using Azure Kubernetes Service enabled by Azure Arc might need to store and retrieve data. For some application workloads, the data can use local, fast storage on an unneeded node when the pods are deleted (Kubernetes uses _pods_ to run an instance of an application).
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‎AKS-Arc/create-clusters-terraform.md

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## Sign in to Azure
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Terraform only supports authenticating to Azure with the Azure CLI using [`az login`](/cli/azure/reference-index#az-login). Authenticating using Azure PowerShell isn't supported. Therefore, while you can use the Azure PowerShell module when doing your Terraform work, you must first [authenticate to Azure](/azure/developer/terraform/authenticate-to-azure):
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Terraform only supports authenticating to Azure with the Azure CLI using [`az login`](/cli/azure/reference-index#az-login). Authenticating using Azure PowerShell isn't supported. Therefore, while you can use the Azure PowerShell module when doing your Terraform work, you must first [authenticate to Azure](/azure/developer/terraform/authenticate-to-azure):
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```azurecli
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az login
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## Implement the Terraform code
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1. Create a directory you can use to test the sample Terraform code, and make it your current directory.
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1. In the same directory, create a file named **providers.tf** and paste the following code:
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1. In the same directory, create a file named **providers.tf** and paste the following code. Make sure to replace `<subscription_ID>` with your subscription ID:
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```terraform
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terraform {
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}
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}
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provider "azurerm" {
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provider "azurerm" {
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subscription_id = "<subscription_ID>"
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features {
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resource_group {
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prevent_deletion_if_contains_resources = false

‎AKS-Arc/deploy-ai-model.md

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@@ -4,7 +4,7 @@ description: Learn how to deploy an AI model on AKS Arc with the Kubernetes AI t
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author: sethmanheim
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ms.author: sethm
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ms.topic: how-to
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ms.date: 03/21/2025
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ms.date: 03/25/2025
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ms.reviewer: haojiehang
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ms.lastreviewed: 12/03/2024
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### [Azure CLI](#tab/azurecli)
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To create a GPU node pool using Azure CLI, run the following command. The GPU VM SKU used in the following example is for the **A16** model; for the full list of VM SKUs, see [Supported VM sizes](scale-requirements.md#supported-vm-sizes).
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To create a GPU node pool using Azure CLI, run the following command. The GPU VM SKU used in the following example is for the **A16** model; for the full list of VM SKUs, see [Supported VM sizes](deploy-gpu-node-pool.md#supported-gpu-vm-sizes).
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```azurecli
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az aksarc nodepool add --name "samplenodepool" --cluster-name "samplecluster" --resource-group "sample-rg" --node-vm-size "Standard_NC16_A16" --os-type "Linux"

‎AKS-Arc/deploy-gpu-node-pool.md

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description: Learn how to deploy GPU-enabled node pools in AKS enabled by Arc.
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author: sethmanheim
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ms.topic: how-to
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ms.date: 03/21/2025
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ms.date: 03/25/2025
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ms.lastreviewed: 03/21/2025
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Graphical Processing Units (GPU) are used for compute-intensive workloads such as machine learning, deep learning, and more. This article describes how to use GPUs for compute-intensive workloads in AKS enabled by Azure Arc.
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[!INCLUDE [supported-gpu-models](includes/supported-gpu-models.md)]
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## Before you begin
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To use GPUs in AKS Arc, make sure you installed the necessary GPU drivers before you begin the deployment of the cluster. Follow the steps in this section.

‎AKS-Arc/includes/supported-gpu-models.md

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---
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author: sethmanheim
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ms.author: sethm
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ms.topic: include
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ms.date: 03/25/2025
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ms.reviewer: abha
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ms.lastreviewed: 03/25/2025
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---
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## Supported GPU models
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The following GPU models are supported by AKS on Azure Local, version 23H2:
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| Manufacturer | GPU model | Supported version |
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|--------------|-----------|-------------------|
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| NVidia | A2 | 2311.2 |
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| NVidia | A16 | 2402.0 |
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| NVidia | T4 | 2408.0 |
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## Supported GPU VM sizes
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The following VM sizes for each GPU model are supported by AKS on Azure Local, version 23H2.
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### Nvidia T4 is supported by NK T4 SKUs
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| VM size | GPUs | GPU Memory: GiB | vCPU | Memory: GiB |
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|-----------------|---|----|-----|----|
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| Standard_NK6 | 1 | 8 | 6 | 12 |
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| Standard_NK12 | 2 | 16 | 12 | 24 |
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### Nvidia A2 is supported by NC2 A2 SKUs
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| VM size | GPUs | GPU Memory: GiB | vCPU | Memory: GiB |
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|-------------------|---|----|----|----|
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| Standard_NC4_A2 | 1 | 16 | 4 | 8 |
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| Standard_NC8_A2 | 1 | 16 | 8 | 16 |
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| Standard_NC16_A2 | 2 | 32 | 16 | 64 |
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| Standard_NC32_A2 | 2 | 32 | 32 | 128 |
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### Nvidia A16 is supported by NC2 A16 SKUs
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| VM size | GPUs | GPU Memory: GiB | vCPU | Memory: GiB |
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|--------------------|---|----|----|----|
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| Standard_NC4_A16 | 1 | 16 | 4 | 8 |
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| Standard_NC8_A16 | 1 | 16 | 8 | 16 |
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| Standard_NC16_A16 | 2 | 32 | 16 | 64 |
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| Standard_NC32_A16 | 2 | 32 | 32 | 128 |

‎AKS-Arc/scale-requirements.md

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title: Scale requirements for AKS on Azure Local
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description: Learn about scale requirements for AKS on Azure Local.
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ms.topic: conceptual
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ms.date: 03/24/2025
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ms.date: 03/25/2025
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For more worker node sizes with GPU support, see the next section.
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## Supported GPU models
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The following GPU models are supported by AKS on Azure Local, version 23H2:
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| Manufacturer | GPU model | Supported version |
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|--------------|-----------|-------------------|
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| NVidia | A2 | 2311.2 |
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| NVidia | A16 | 2402.0 |
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| NVidia | T4 | 2408.0 |
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## Supported VM sizes
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The following VM sizes for each GPU models are supported by AKS on Azure Local, version 23H2.
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### Nvidia T4 is supported by NK T4 SKUs
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| VM size | GPUs | GPU Memory: GiB | vCPU | Memory: GiB |
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|-----------------|---|----|-----|----|
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| Standard_NK6 | 1 | 8 | 6 | 12 |
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| Standard_NK12 | 2 | 16 | 12 | 24 |
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### Nvidia A2 is supported by NC2 A2 SKUs
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| VM size | GPUs | GPU Memory: GiB | vCPU | Memory: GiB |
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|-------------------|---|----|----|----|
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| Standard_NC4_A2 | 1 | 16 | 4 | 8 |
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| Standard_NC8_A2 | 1 | 16 | 8 | 16 |
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| Standard_NC16_A2 | 2 | 32 | 16 | 64 |
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| Standard_NC32_A2 | 2 | 32 | 32 | 128 |
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### Nvidia A16 is supported by NC2 A16 SKUs
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| VM size | GPUs | GPU Memory: GiB | vCPU | Memory: GiB |
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|--------------------|---|----|----|----|
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| Standard_NC4_A16 | 1 | 16 | 4 | 8 |
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| Standard_NC8_A16 | 1 | 16 | 8 | 16 |
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| Standard_NC16_A16 | 2 | 32 | 16 | 64 |
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| Standard_NC32_A16 | 2 | 32 | 32 | 128 |
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[!INCLUDE [supported-gpu-models](includes/supported-gpu-models.md)]
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## Next steps
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‎azure-local/TOC.yml

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- name: Via console
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href: deploy/deployment-arc-register-server-permissions.md
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- name: Via local UI
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href: deploy/deployment-arc-register-local-ui.md
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- name: Via Configurator app
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href: deploy/deployment-arc-register-configurator-app.md
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- name: 5. Choose a deployment method
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- name: 5A. Deploy via Azure portal

‎azure-local/concepts/compare-windows-server.md

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ms.date: 03/28/2025
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# Compare Azure Local to Windows Server
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This article explains key differences between Azure Local and Windows Server and provides guidance about when to use each. Both products are actively supported and maintained by Microsoft. Many organizations choose to deploy both as they are intended for different and complementary purposes.
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When replacing a datacenter primarily running VMware, Azure Local is typically not the optimal solution, as it is better suited for specific distributed scenarios, like retail stores, manufacturing facilities, or branch offices. Azure Local is optimized for distributed sites with fewer than three clusters of 16 nodes, especially when there are latency or regulatory constraints. Instead, we recommend [Azure VMware Solution](/azure/azure-vmware), which is specifically designed for VMware workload modernization and datacenter replacement.
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## When to use Azure Local
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Azure Local is Microsoft's premier hyperconverged infrastructure platform for running VMs or virtual desktops on-premises with connections to Azure hybrid services. Azure Local can help to modernize and secure your datacenters and branch offices, and achieve industry-best performance with low latency and data sovereignty.

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