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Azure Local + GPUs: On-Prem AI for Regulated Industries

Paul Bryant

Table of Contents

  1. Introduction
  2. The Shift: Why On-Prem AI Now?
  3. Azure Local & GPUs: Core Architecture
    • 3.1 Azure Stack Edge vs. Azure Local (Stack HCI)
    • 3.2 NVIDIA GPU Integration
    • 3.3 Hybrid & Edge Design Patterns
  4. Data Sovereignty, Compliance & Security
  5. Use Cases by Regulated Industry
    • 5.1 Healthcare
    • 5.2 Financial Services
    • 5.3 Government & Defense
    • 5.4 Manufacturing & Critical Infrastructure
  6. Real-World Example: Healthcare Imaging on Azure Stack Edge with NVIDIA
  7. Deployment Walkthrough: Architecture, Commands, and Diagrams
  8. Future Trends: Federated & Confidential AI
  9. Conclusion

1. Introduction

AI is transforming how regulated industries operate. However, these sectors face strict data privacy, residency, and compliance constraints. Moving sensitive workloads to the public cloud is not always possible or even legal. Azure Local, which includes Azure Stack HCI and Edge, with NVIDIA GPUs is a robust, on-premises AI platform from Microsoft. This solution empowers organizations to run high-performance AI workloads where data lives without sacrificing compliance.

This guide covers:

2. The Shift: Why On-Prem AI Now?

The push for on-premises AI is not just a trend. It is a necessity.

Azure Local with GPUs uniquely enables these organizations to embrace AI without compromise.

3. Azure Local & GPUs: Core Architecture

3.1 Azure Stack Edge vs. Azure Local (Stack HCI)

Key Difference: Stack Edge is turnkey for rapid edge or remote deployment. HCI is a scalable, datacenter-class foundation with deep virtualization and storage features.

3.2 NVIDIA GPU Integration

3.3 Hybrid & Edge Design Patterns

Typical Hybrid Architecture

Flow:
Models can be trained in Azure and deployed to on-prem edge for inference. All workloads can also be fully run and managed on-premises. Data stays local for sovereignty and compliance. Azure Arc enables management, monitoring, and update control from the cloud.

4. Data Sovereignty, Compliance & Security

Key Regulatory Drivers

Azure Local Compliance Features

5. Use Cases by Regulated Industry

5.1 Healthcare

5.2 Financial Services

5.3 Government & Defense

5.4 Manufacturing & Critical Infrastructure

6. Real-World Example: Healthcare Imaging on Azure Stack Edge with NVIDIA

Microsoft Case Study: AI-Accelerated Healthcare Imaging

Diagram: Imaging Workflow.

Note: Actual performance, such as inference speed and throughput, depends on model size, imaging workload, GPU model, and site configuration. Refer to published benchmarks for precise figures.

7. Deployment Walkthrough: Architecture, Commands, and Diagrams

7.1 Deploying an Azure Local GPU-Accelerated VM (PowerShell Example)

# Import HCI module
Import-Module -Name Az.StackHCI

# Define VM and GPU passthrough
$vmName = "HealthcareImagingAI"
$gpuName = "NVIDIA-A100"
New-VM -Name $vmName -MemoryStartupBytes 32GB -Generation 2 -Path "D:\VMs"

# Add GPU to VM
Add-VMAssignableDevice -VMName $vmName -LocationPath (Get-PnpDevice -FriendlyName $gpuName).LocationPaths[0]

# Start VM
Start-VM -Name $vmName

7.2 Deploying a Model with Azure Arc & IoT Edge (YAML Excerpt)

# Azure IoT Edge deployment for AI model
modules:
  ai_inference_module:
    image: myacr.azurecr.io/healthcare-ai-inference:latest
    createOptions: |
      {
        "HostConfig": {
          "DeviceRequests": [
            {
              "Driver": "nvidia",
              "Count": -1,
              "Capabilities": [["gpu"]]
            }
          ]
        }
      }

7.3 Architecture: Cross-Site Edge AI

8. Future Trends: Federated & Confidential AI

9. Conclusion

Azure Local with NVIDIA GPUs delivers a best-in-class solution for regulated industries seeking on-premises AI without compliance tradeoffs. With architectural flexibility, strong data sovereignty, and seamless integration with Azure management, regulated organizations can unlock the full value of AI anywhere data lives.

Disclaimer

The views expressed in this article are those of the author and do not represent the opinions of Microsoft, my employer or any affiliated organization. Always refer to the official Microsoft documentation before production deployment.

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