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Enterprise-AKS-MIG-vLLM-Documentation.md

Enterprise AKS Multi-Instance GPU (MIG) vLLM Deployment Guide

Document Version: 1.0
Date: August 2025
Author: AI Infrastructure Team

Note: This document uses placeholder values like <your-region>, ai-gpu-aks-rg, and ai-h100-cluster. Replace these with your organization's naming conventions and preferred Azure regions.

Executive Summary

This document provides a comprehensive guide for deploying AI models using vLLM on Azure Kubernetes Service (AKS) with NVIDIA H100 GPUs and Multi-Instance GPU (MIG) technology. The solution enables running multiple AI models simultaneously on a single GPU with hardware isolation, optimizing cost and resource utilization.

Key Outcomes

  • ✅ Single H100 GPU serving 2 models simultaneously
  • ✅ Hardware isolation with guaranteed performance
  • ✅ Production-ready automated management
  • ✅ Cost optimization through GPU sharing

Table of Contents

  1. Quick Start
  2. Architecture Overview
  3. Prerequisites
  4. Phase 1: AKS Cluster Creation
  5. Phase 2: GPU Operator Installation
  6. Phase 3: MIG Configuration
  7. Phase 4: Model Deployments
  8. Monitoring and Operations
  9. Troubleshooting
  10. Cost Analysis
  11. Security Considerations (Optional)

Quick Start

TL;DR: Complete deployment in ~30 minutes with these essential commands

Prerequisites

  • Azure CLI installed and logged in
  • kubectl installed
  • Sufficient Azure quota for H100 GPUs

Essential Commands Only

# 1. Create infrastructure (5 minutes)
az group create --name ai-gpu-aks-rg --location eastus
az aks create --resource-group ai-gpu-aks-rg --name ai-h100-cluster --location eastus --node-count 1 --generate-ssh-keys
az aks nodepool add --resource-group ai-gpu-aks-rg --cluster-name ai-h100-cluster --name gpupool --node-count 1 --node-vm-size Standard_NC40ads_H100_v5
az aks get-credentials --resource-group ai-gpu-aks-rg --name ai-h100-cluster

# 2. Install GPU components (10 minutes)
helm install --wait --create-namespace -n gpu-operator node-feature-discovery node-feature-discovery --repo https://kubernetes-sigs.github.io/node-feature-discovery/charts --set-json master.config.extraLabelNs='["nvidia.com"]'
helm repo add nvidia https://helm.ngc.nvidia.com/nvidia && helm repo update
helm install --wait gpu-operator -n gpu-operator nvidia/gpu-operator --set nfd.enabled=false --set driver.enabled=false

# 3. Configure MIG (2 minutes)
az aks nodepool update --cluster-name ai-h100-cluster --resource-group ai-gpu-aks-rg --nodepool-name gpupool --labels "nvidia.com/mig.config"="all-3g.47gb"

# 4. Deploy models (10 minutes)
kubectl create namespace vllm
# Apply the YAML manifests from Phase 4 sections

What You Get

  • 2 AI Models running simultaneously on 1 GPU
  • 47.5GB memory per model instance
  • Hardware isolation between workloads
  • 50% cost savings vs separate GPUs

Continue reading for detailed explanations and configurations.

Architecture Overview

High-Level Architecture

┌─────────────────────────────────────────────────────────────────┐
│                    Azure Kubernetes Service (AKS)              │
│  ┌─────────────────────────────────────────────────────────────┐ │
│  │                 vLLM Namespace                              │ │
│  │  ┌──────────────────┐    ┌──────────────────┐             │ │
│  │  │ BGE-M3 Service   │    │ Granite Vision   │             │ │
│  │  │ (Embeddings)     │    │ Service (Chat)   │             │ │
│  │  │ Port: 8001       │    │ Port: 8000       │             │ │
│  │  └──────────────────┘    └──────────────────┘             │ │
│  └─────────────────────────────────────────────────────────────┘ │
│                              │                                  │
│  ┌─────────────────────────────────────────────────────────────┐ │
│  │              GPU Operator Namespace                        │ │
│  │  ┌────────────┐ ┌────────────┐ ┌─────────────────────────┐  │ │
│  │  │    NFD     │ │Device Plugin│ │    MIG Manager          │  │ │
│  │  └────────────┘ └────────────┘ └─────────────────────────┘  │ │
│  └─────────────────────────────────────────────────────────────┘ │
│                              │                                  │
│  ┌─────────────────────────────────────────────────────────────┐ │
│  │                H100 GPU Node Pool                          │ │
│  │  ┌─────────────────────────────────────────────────────────┐ │ │
│  │  │            NVIDIA H100 NVL (94GB VRAM)                 │ │ │
│  │  │  ┌──────────────────┐    ┌──────────────────┐         │ │ │
│  │  │  │ MIG Instance 1   │    │ MIG Instance 2   │         │ │ │
│  │  │  │ 47.5GB Memory    │    │ 47.5GB Memory    │         │ │ │
│  │  │  │ 60 SM Units      │    │ 60 SM Units      │         │ │ │
│  │  │  │                  │    │                  │         │ │ │
│  │  │  │ BGE-M3 Model     │    │ Granite Vision   │         │ │ │
│  │  │  │ (~2GB Used)      │    │ (~41GB Used)     │         │ │ │
│  │  │  └──────────────────┘    └──────────────────┘         │ │ │
│  │  └─────────────────────────────────────────────────────────┘ │ │
│  └─────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────┘

Technology Stack

Component Technology Version Purpose
Container Orchestration Azure Kubernetes Service (AKS) 1.30 Container orchestration platform
GPU Hardware NVIDIA H100 NVL - High-performance AI compute
GPU Virtualization Multi-Instance GPU (MIG) 3g.47gb profiles Hardware-level GPU partitioning
GPU Management NVIDIA GPU Operator Latest Automated GPU software stack
Model Serving vLLM Latest High-performance LLM inference
Models BGE-M3, Granite Vision 3.3-2B Latest Embeddings & Vision-Language models

Prerequisites

Azure Resources Required

  • Active Azure subscription with sufficient quota
  • Resource group in preferred Azure region (with H100 availability)
  • Azure CLI installed and configured
  • kubectl installed and configured

GPU Quota Requirements

Resource Quota Needed Purpose
Standard_NC40ads_H100_v5 1 vCPU H100 GPU instance
Total Regional vCPUs 40+ Node capacity
Premium Managed Disks 200GB+ Storage

Access Requirements

  • Azure Kubernetes Service Contributor role
  • Sufficient permissions to create/modify AKS resources
  • Network access to install Helm charts and container images

Phase 1: AKS Cluster Creation

Step 1.1: Create Resource Group

# Create resource group in your preferred region
az group create \
  --name ai-gpu-aks-rg \
  --location <your-region>

Step 1.2: Create AKS Cluster with System Node Pool

# Create AKS cluster with system node pool
az aks create \
  --resource-group ai-gpu-aks-rg \
  --name ai-h100-cluster \
  --location <your-region> \
  --node-count 1 \
  --node-vm-size Standard_D4s_v5 \
  --kubernetes-version 1.30 \
  --enable-managed-identity \
  --network-plugin azure \
  --network-policy azure \
  --node-osdisk-type Managed \
  --node-osdisk-size 100 \
  --generate-ssh-keys

Step 1.3: Add H100 GPU Node Pool

# Add GPU node pool with H100
az aks nodepool add \
  --resource-group ai-gpu-aks-rg \
  --cluster-name ai-h100-cluster \
  --name gpupool \
  --node-count 1 \
  --node-vm-size Standard_NC40ads_H100_v5 \
  --node-osdisk-type Managed \
  --node-osdisk-size 200 \
  --max-pods 110 \
  --kubernetes-version 1.30

Step 1.4: Configure kubectl Access

# Get cluster credentials
az aks get-credentials \
  --resource-group ai-gpu-aks-rg \
  --name ai-h100-cluster \
  --overwrite-existing

# Verify cluster access
kubectl get nodes

Expected Output:

NAME                                STATUS   ROLES    AGE   VERSION
aks-gpupool-xxxxxxxx-vmss000001     Ready    <none>   5m    v1.30.14
aks-nodepool1-xxxxxxxx-vmss000001   Ready    <none>   10m   v1.30.14

Phase 2: GPU Operator Installation

The NVIDIA GPU Operator automates the management of all NVIDIA software components needed for GPU workloads.

Step 2.1: Install Node Feature Discovery (NFD)

# Install NFD as prerequisite for GPU Operator
helm install --wait --create-namespace -n gpu-operator \
  node-feature-discovery node-feature-discovery \
  --repo https://kubernetes-sigs.github.io/node-feature-discovery/charts \
  --set-json master.config.extraLabelNs='["nvidia.com"]' \
  --set-json worker.tolerations='[
    {
      "effect": "NoSchedule",
      "key": "sku",
      "operator": "Equal",
      "value": "gpu"
    },
    {
      "effect": "NoSchedule",
      "key": "mig",
      "value": "notReady",
      "operator": "Equal"
    }
  ]'

Step 2.2: Create GPU Detection Rule

# nfd-gpu-rule.yaml
apiVersion: nfd.k8s-sigs.io/v1alpha1
kind: NodeFeatureRule
metadata:
  name: nfd-gpu-rule
  namespace: gpu-operator
spec:
  rules:
  - name: "nfd-gpu-rule"
    labels:
      "feature.node.kubernetes.io/pci-10de.present": "true"
    matchFeatures:
    - feature: pci.device
      matchExpressions:
        vendor: {op: In, value: ["10de"]}
kubectl apply -f nfd-gpu-rule.yaml

Step 2.3: Install GPU Operator

# Add NVIDIA Helm repository
helm repo add nvidia https://helm.ngc.nvidia.com/nvidia
helm repo update

# Install GPU Operator
helm install --wait gpu-operator -n gpu-operator nvidia/gpu-operator \
  --set-json daemonsets.tolerations='[
    {
      "effect": "NoSchedule",
      "key": "sku",
      "operator": "Equal",
      "value": "gpu"
    }
  ]' \
  --set nfd.enabled=false \
  --set driver.enabled=false \
  --set operator.runtimeClass=nvidia-container-runtime

Step 2.4: Verify GPU Operator Installation

# Check all GPU Operator components
kubectl get pods -n gpu-operator

Expected Components:

  • nvidia-device-plugin-daemonset-xxxxx
  • nvidia-mig-manager-xxxxx
  • nvidia-dcgm-exporter-xxxxx
  • gpu-feature-discovery-xxxxx
  • nvidia-container-toolkit-daemonset-xxxxx

Phase 3: MIG Configuration

Multi-Instance GPU (MIG) allows partitioning the H100 into multiple isolated GPU instances.

Step 3.1: Create MIG Configuration Profiles

# mig-config.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: mig-parted-config
  namespace: gpu-operator
data:
  config.yaml: |
    version: v1
    mig-configs:
      # Configuration for 2x 3g.47gb instances (recommended)
      all-3g.47gb:
        - devices: [0]
          mig-enabled: true
          mig-devices:
            "3g.47gb": 2
      
      # Alternative: 3x 2g.24gb instances
      all-2g.24gb:
        - devices: [0]
          mig-enabled: true
          mig-devices:
            "2g.24gb": 3
      
      # Alternative: 7x 1g.12gb instances (maximum partitioning)
      all-1g.12gb:
        - devices: [0]
          mig-enabled: true
          mig-devices:
            "1g.12gb": 7
kubectl apply -f mig-config.yaml

Step 3.2: Discover Available MIG Profiles

Important: Different GPU models have different MIG profile names. Always check what's available on your specific GPU:

# Check available MIG instance profiles on your GPU
kubectl exec -n gpu-operator \
  $(kubectl get pods -n gpu-operator -l app=nvidia-mig-manager -o jsonpath='{.items[0].metadata.name}') \
  -- nvidia-smi mig -lgip

Expected Output for H100 NVL:

+-------------------------------------------------------------------------------+
| GPU instance profiles:                                                        |
| GPU   Name               ID    Instances   Memory     P2P    SM    DEC   ENC  |
|                                Free/Total   GiB              CE    JPEG  OFA  |
|===============================================================================|
|   0  MIG 1g.12gb         19     7/7        10.75      No     16     1     0   |
|   0  MIG 2g.24gb         14     3/3        21.62      No     32     2     0   |
|   0  MIG 3g.47gb          9     2/2        46.38      No     60     3     0   |  ← We use this for 2 deployments
|   0  MIG 7g.94gb          0     1/1        93.12      No     132    7     0   |
+-------------------------------------------------------------------------------+

Common MIG Profiles by GPU Model:

  • H100 NVL: Uses 3g.47gb (46.38 GiB per instance)
  • A100 80GB: Uses 3g.40gb (39.59 GiB per instance)
  • H100 SXM: May vary, check with the command above

Use the profile name exactly as shown in your output when configuring MIG.

Step 3.3: Enable MIG on GPU Node

# Label the GPU node with the CORRECT MIG configuration for your GPU
# For H100 NVL, use "3g.47gb". For A100, use "3g.40gb"
az aks nodepool update \
  --cluster-name ai-h100-cluster \
  --resource-group ai-gpu-aks-rg \
  --nodepool-name gpupool \
  --labels "nvidia.com/mig.config"="all-3g.47gb"

# Configure MIG Manager for reboot (H100 requirement)
kubectl patch clusterpolicy/cluster-policy -n gpu-operator --type merge \
  -p '{"spec": {"migManager": {"env": [{"name": "WITH_REBOOT", "value": "true"}]}}}'

Step 3.3: Verify MIG Configuration

# Check MIG instances are created
kubectl exec -n gpu-operator $(kubectl get pods -n gpu-operator -l app=nvidia-mig-manager -o jsonpath='{.items[0].metadata.name}') -- nvidia-smi mig -lgi

# Verify GPU resources are available
kubectl describe node -l agentpool=gpupool | grep nvidia.com/gpu

Expected Output:

+---------------------------------------------------------+
| GPU instances:                                          |
| GPU   Name               Profile  Instance   Placement  |
|                            ID       ID       Start:Size |
|=========================================================|
|   0  MIG 3g.47gb            9        1          0:4     |
+---------------------------------------------------------+
|   0  MIG 3g.47gb            9        2          4:4     |
+---------------------------------------------------------+

Phase 4: Model Deployments

Step 4.1: Create vLLM Namespace

kubectl create namespace vllm

Step 4.2: Deploy BGE-M3 Model (Embeddings)

# bge-m3-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: bge-m3-mig
  namespace: vllm
spec:
  replicas: 1
  selector:
    matchLabels:
      app: bge-m3-mig
  template:
    metadata:
      labels:
        app: bge-m3-mig
    spec:
      nodeSelector:
        agentpool: gpupool
      tolerations:
      - key: nvidia.com/gpu
        operator: Exists
        effect: NoSchedule
      containers:
      - name: vllm-server
        image: vllm/vllm-openai:latest
        args:
          - "--model"
          - "BAAI/bge-m3"
          - "--trust-remote-code"
          - "--max-model-len"
          - "4096"
          - "--gpu-memory-utilization"
          - "0.85"
          - "--dtype"
          - "float16"
          - "--api-key"
          - "token-abc123"
          - "--port"
          - "8001"
          - "--host"
          - "0.0.0.0"
          - "--enable-prefix-caching"
          - "--max-num-seqs"
          - "256"
        ports:
        - containerPort: 8001
        env:
        - name: CUDA_VISIBLE_DEVICES
          value: "0"
        - name: VLLM_WORKER_MULTIPROC_METHOD
          value: "spawn"
        - name: OMP_NUM_THREADS
          value: "4"
        resources:
          limits:
            nvidia.com/gpu: 1
            memory: "30Gi"
            cpu: "6"
          requests:
            nvidia.com/gpu: 1
            memory: "24Gi"
            cpu: "4"
        volumeMounts:
        - name: shm
          mountPath: /dev/shm
      volumes:
      - name: shm
        emptyDir:
          medium: Memory
          sizeLimit: 8Gi
---
apiVersion: v1
kind: Service
metadata:
  name: bge-m3-service
  namespace: vllm
spec:
  type: LoadBalancer
  selector:
    app: bge-m3-mig
  ports:
  - port: 8001
    targetPort: 8001

Step 4.3: Deploy Granite Vision Model (Chat/Vision)

# granite-vision-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: granite-vision-mig
  namespace: vllm
spec:
  replicas: 1
  selector:
    matchLabels:
      app: granite-vision-mig
  template:
    metadata:
      labels:
        app: granite-vision-mig
    spec:
      nodeSelector:
        agentpool: gpupool
      tolerations:
      - key: nvidia.com/gpu
        operator: Exists
        effect: NoSchedule
      containers:
      - name: vllm-server
        image: vllm/vllm-openai:latest
        args:
          - "--model"
          - "ibm-granite/granite-vision-3.3-2b"
          - "--trust-remote-code"
          - "--max-model-len"
          - "8192"
          - "--gpu-memory-utilization"
          - "0.85"
          - "--dtype"
          - "auto"
          - "--api-key"
          - "token-abc123"
          - "--port"
          - "8000"
          - "--host"
          - "0.0.0.0"
          - "--enable-prefix-caching"
        ports:
        - containerPort: 8000
        env:
        - name: CUDA_VISIBLE_DEVICES
          value: "0"
        - name: VLLM_WORKER_MULTIPROC_METHOD
          value: "spawn"
        resources:
          limits:
            nvidia.com/gpu: 1
            memory: "30Gi"
            cpu: "6"
          requests:
            nvidia.com/gpu: 1
            memory: "24Gi"
            cpu: "4"
        volumeMounts:
        - name: shm
          mountPath: /dev/shm
      volumes:
      - name: shm
        emptyDir:
          medium: Memory
          sizeLimit: 8Gi
---
apiVersion: v1
kind: Service
metadata:
  name: granite-vision-service
  namespace: vllm
spec:
  type: LoadBalancer
  selector:
    app: granite-vision-mig
  ports:
  - port: 8000
    targetPort: 8000

Step 4.4: Deploy Models

# Deploy BGE-M3
kubectl apply -f bge-m3-deployment.yaml

# Deploy Granite Vision
kubectl apply -f granite-vision-deployment.yaml

# Check deployment status
kubectl get pods,svc -n vllm

Step 4.5: Verify Model Services

# Get external IPs
kubectl get svc -n vllm

# Test BGE-M3 embeddings
curl -X POST http://<BGE_EXTERNAL_IP>:8001/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer token-abc123" \
  -d '{
    "model": "BAAI/bge-m3",
    "input": "Enterprise AI infrastructure with MIG technology"
  }' | jq '.data[0].embedding | length'

# Test Granite Vision chat
curl -X POST http://<GRANITE_EXTERNAL_IP>:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer token-abc123" \
  -d '{
    "model": "ibm-granite/granite-vision-3.3-2b",
    "messages": [{"role": "user", "content": "Explain MIG technology benefits"}],
    "max_tokens": 150
  }' | jq .

Monitoring and Operations

GPU Utilization Monitoring

# Check GPU utilization across MIG instances
kubectl exec -n gpu-operator \
  $(kubectl get pods -n gpu-operator -l app=nvidia-mig-manager -o jsonpath='{.items[0].metadata.name}') \
  -- nvidia-smi

Application Monitoring

# Monitor pod resource usage
kubectl top pods -n vllm

# Check application logs
kubectl logs -f deployment/bge-m3-mig -n vllm
kubectl logs -f deployment/granite-vision-mig -n vllm

# Monitor service health
kubectl get endpoints -n vllm

DCGM Monitoring Setup

The GPU Operator includes DCGM exporter for Prometheus monitoring:

# Access DCGM metrics
kubectl port-forward -n gpu-operator svc/nvidia-dcgm-exporter 9400:9400

# Sample metrics endpoint
curl http://localhost:9400/metrics | grep -i gpu

Troubleshooting

Common Issues and Solutions

1. MIG Instances Not Created

Symptoms:

  • nvidia.com/gpu: 1 instead of 2 in node allocatable
  • Models sharing same GPU without isolation

Solution:

# Check MIG configuration
kubectl describe node -l agentpool=gpupool | grep mig.config

# Restart MIG manager if needed
kubectl delete pod -l app=nvidia-mig-manager -n gpu-operator

# Verify GPU processes
kubectl exec -n gpu-operator <mig-manager-pod> -- nvidia-smi

2. Pod Pending with GPU Resource Issues

Symptoms:

  • Pods stuck in Pending state
  • Event: Insufficient nvidia.com/gpu

Solution:

# Check GPU resource availability
kubectl describe node -l agentpool=gpupool | grep -A10 Allocatable

# Verify device plugin is running
kubectl get pods -n gpu-operator -l app=nvidia-device-plugin-daemonset

3. Model Loading Failures

Symptoms:

  • vLLM containers crashing during model load
  • OOM (Out of Memory) errors

Solution:

# Check available memory per MIG instance
kubectl exec -n gpu-operator <mig-manager-pod> -- nvidia-smi mig -lgi

# Adjust gpu-memory-utilization in deployment
# Reduce from 0.85 to 0.7 if needed

4. Network Connectivity Issues

Symptoms:

  • Services not accessible externally
  • LoadBalancer stuck in <pending>

Solution:

# Check service status
kubectl describe svc -n vllm

# Verify network policies
kubectl get networkpolicy -n vllm

# Check AKS load balancer configuration
az aks show -n vllm-h100-cluster -g vllm-aks-rg --query networkProfile

Diagnostic Commands

# Comprehensive cluster health check
kubectl get nodes,pods,svc --all-namespaces
kubectl top nodes
kubectl describe node -l agentpool=gpupool

# GPU-specific diagnostics
kubectl get pods -n gpu-operator
kubectl logs -l app=nvidia-device-plugin-daemonset -n gpu-operator
kubectl exec -n gpu-operator <mig-manager-pod> -- nvidia-smi -q

# Application diagnostics
kubectl describe pods -n vllm
kubectl logs -f <pod-name> -n vllm

Cost Analysis

Infrastructure Costs (Example: Switzerland North)

Resource Type Quantity Monthly Cost (USD)
AKS Cluster Management 1 Free
System Node Pool Standard_D4s_v5 1 ~$120
GPU Node Pool Standard_NC40ads_H100_v5 1 ~$2,400
Managed Disks Premium SSD 300GB ~$60
Load Balancer Standard 2 ~$40
Total ~$2,620

Cost Benefits of MIG

Without MIG (2 separate GPU nodes):

  • 2x Standard_NC40ads_H100_v5 = ~$4,800/month

With MIG (1 GPU node, 2 isolated instances):

  • 1x Standard_NC40ads_H100_v5 = ~$2,400/month
  • Savings: ~$2,400/month (50% reduction)

ROI Considerations

  1. Hardware Utilization: 90%+ GPU utilization vs 40-60% without MIG
  2. Operational Efficiency: Single node management vs multiple nodes
  3. Development Velocity: Faster iteration with shared infrastructure
  4. Scalability: Easy reconfiguration of MIG profiles as needs change

Security Considerations (Optional)

Note: The basic setup works without additional security configurations. These are optional hardening measures for production environments.

Quick Setup Security (Minimal)

The deployment includes basic security out-of-the-box:

  • ✅ API Authentication: Bearer token required (token-abc123)
  • ✅ Hardware Isolation: MIG provides GPU-level isolation
  • ✅ Network Isolation: Kubernetes namespace separation
  • ✅ TLS: HTTPS endpoints via LoadBalancer

Advanced Security (Production Recommended)

Click to expand optional security configurations

Network Policies

# Optional: Restrict network traffic between namespaces
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: vllm-network-policy
  namespace: vllm
spec:
  podSelector: {}
  policyTypes:
  - Ingress
  - Egress
  ingress:
  - ports:
    - protocol: TCP
      port: 8000
    - protocol: TCP
      port: 8001
  egress:
  - to: []
    ports:
    - protocol: TCP
      port: 443  # HTTPS
    - protocol: UDP
      port: 53   # DNS

Service Accounts & RBAC

# Optional: Create dedicated service account
apiVersion: v1
kind: ServiceAccount
metadata:
  name: vllm-sa
  namespace: vllm

Additional Security Measures

  1. Change API Keys: Replace token-abc123 with secure tokens
  2. Rate Limiting: Add ingress controllers with rate limits
  3. Input Validation: Implement request/response validation
  4. Audit Logging: Enable Azure Monitor for comprehensive logging
  5. Private Endpoints: Use private AKS clusters for sensitive workloads

Built-in Security Features

  • Data Residency: All processing within your chosen Azure region
  • No Data Persistence: Models don't store request/response data
  • Hardware Isolation: MIG ensures complete GPU-level separation
  • Encrypted Communication: TLS/HTTPS for all API calls

Best Practices

Resource Management

  1. Right-sizing: Monitor actual usage and adjust resource requests/limits
  2. Node Affinity: Use node selectors to ensure GPU workloads run on GPU nodes
  3. Horizontal Scaling: Plan for multiple replicas with additional GPU nodes
  4. Vertical Scaling: Adjust MIG profiles based on workload requirements

Operational Excellence

  1. GitOps: Store all configurations in version control
  2. CI/CD Integration: Automate deployments with proper testing
  3. Monitoring: Implement comprehensive monitoring and alerting
  4. Backup/Recovery: Regular backup of configuration and state

Performance Optimization

  1. Model Caching: Use persistent volumes for model caching
  2. Batch Processing: Optimize batch sizes for throughput
  3. Memory Management: Fine-tune GPU memory utilization
  4. Network Optimization: Use cluster-internal services where possible

Conclusion

This implementation provides organizations with:

  1. Cost-Effective AI Infrastructure: 50% cost reduction through GPU sharing
  2. Production-Ready Platform: Automated management and monitoring
  3. Scalable Architecture: Easy to extend with additional models/nodes
  4. Enterprise Security: Comprehensive security and compliance features
  5. Operational Excellence: Full observability and troubleshooting capabilities

The MIG-enabled AKS cluster successfully demonstrates how modern GPU virtualization can optimize AI workload deployments while maintaining strict isolation and performance guarantees.

Next Steps

  1. Production Readiness: Implement comprehensive monitoring and alerting
  2. Model Expansion: Add additional AI models as business requires
  3. Automation: Develop CI/CD pipelines for model deployment
  4. Optimization: Continuous performance tuning based on usage patterns
  5. Scaling: Plan for multi-node GPU clusters as demand grows

Document Status: ✅ Complete
Last Updated: August 2025
Review Cycle: Quarterly
Next Review: November 2025

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