Private AI Cloud Architecture

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Private AI Cloud Architecture
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Private AI Cloud Architecture: Building Confidential AI Infrastructure

Meta Description: Complete architectural guide for building private AI clouds with GPU TEE, confidential VMs, and secure model deployment. Learn design patterns for confidential AI at scale.

Target Keywords: private AI cloud architecture, confidential AI infrastructure, GPU TEE architecture, secure AI deployment, AI cloud design patterns

Reading Time: 22 minutes

TL;DR - Private AI Cloud Architecture

Key Architecture Components:

  1. GPU TEE Layer: NVIDIA H100/H200 with confidential computing for AI workloads
  2. Orchestration Layer: Kubernetes with TEE-aware scheduling
  3. Attestation Layer: Continuous verification and trust management
  4. Data Layer: Encrypted storage with TEE-integrated key management
  5. API Layer: Secure endpoints with attestation verification

Reference Implementation: Phala Cloud provides production-ready private AI infrastructure with all layers integrated

Best For: Organizations deploying confidential AI at scale (training, inference, model serving)

Architecture Overview

Private AI Cloud Layers

Layer 1: Infrastructure Foundation

Hardware Requirements

Compute Nodes (GPU TEE):

  • CPU: Intel Xeon 4th Gen (TDX) or AMD EPYC 4th Gen (SEV-SNP)
  • GPU: NVIDIA H100 (80GB) or H200 (141GB) with Confidential Computing
  • RAM: 512GB-2TB DDR5 (ECC)
  • Storage: 4x 4TB NVMe SSD (encrypted)
  • Network: 2x 400Gbps InfiniBand or RoCE

Typical Cluster:

  • 4-16 GPU nodes (32-128 GPUs total)
  • High-bandwidth GPU interconnect (NVLink, NVSwitch)
  • Dedicated management network

Network Architecture:

Phala Cloud Infrastructure

Advantage: Fully managed infrastructure with TEE-optimized hardware

  • Infrastructure:
  • H100/H200 GPU nodes with confidential computing
  • Intel TDX / AMD SEV-SNP CPU TEE
  • Encrypted NVMe storage
  • High-speed networking
  • Automated provisioning

Layer 2: Confidential Compute

GPU TEE Configuration

NVIDIA H100 Confidential Computing Mode:

apiVersion: v1
kind: Pod
metadata:
  name: confidential-ai-training
spec:
  containers:
  - name: trainer
    image: pytorch/pytorch:latest
    resources:
      limits:
        nvidia.com/gpu: 4  # 4x H100 GPUs
    env:
    - name: NVIDIA_CONFIDENTIAL_COMPUTING
      value: "enabled"
    - name: GPU_MEMORY_ENCRYPTION
      value: "aes-256-gcm"
    volumeMounts:
    - name: dstack-socket
      mountPath: /var/run/dstack.sock  # For attestation
  volumes:
  - name: dstack-socket
    hostPath:
      path: /var/run/dstack.sock

What Gets Encrypted:

  • GPU Memory (HBM): All model weights, activations, gradients
  • PCIe Traffic: Data transfer between CPU and GPU
  • Storage I/O: Model checkpoints, datasets

Performance Impact: 5-15% overhead in TEE mode (Phala Cloud optimizes to <10%)

Memory and Storage Encryption

Encrypted Volumes:

volumes:
  model-storage:
    type: encrypted-nvme
    size: 500GB
    encryption: aes-256-xts
    key_management: decentralized-kms  # Phala's key manager
  dataset-storage:
    type: encrypted-nvme
    size: 5TB
    encryption: aes-256-xts

Key Management (Phala Approach):

  • Keys derived from hardware root of trust
  • Decentralized KMS (no single point of key compromise)
  • Keys bound to specific TEE instances (portable across hardware)

Layer 3: Attestation & Trust

Continuous Attestation Architecture

Phala Cloud Implementation:

from phala_sdk import verify_attestation

app_id = "confidential-llm-training"
attestation = get_attestation_report(app_id)

if verify_attestation(attestation):
    print("✅ TEE verified:")
    upload_training_data(app_id, "medical-images.enc")
else:
    raise SecurityError("Attestation failed!")

What Gets Attested:

  1. Hardware Identity: Genuine NVIDIA H100, Intel TDX, or AMD SEV
  2. Software Measurements:
  • Docker image hash
  • Environment variables (hashed)
  • Startup configuration
  1. Runtime State: No tampering since boot

Policy Enforcement

Attestation Policy Example:

policy:
  name: "HIPAA-compliant-AI-training"
  required_tee:
    - nvidia-h100-confidential
    - intel-tdx
  allowed_images:
    - "pytorch/pytorch@sha256:abc123..."
  forbidden_env_vars:
    - "DEBUG=true"
  continuous_verification:
    interval: 300s
  on_failure:
    action: "terminate"
    alert: "[email protected]"

Layer 4: Orchestration & Scheduling

Kubernetes for Confidential AI

Architecture:

TEE-Aware Scheduling:

apiVersion: v1
kind: Pod
metadata:
  name: llama-70b-training
  labels:
    confidential: "required"
    attestation: "nvidia-h100"
spec:
  nodeSelector:
    tee.nvidia.com/confidential: "true"
    gpu.nvidia.com/model: "H100"
  containers:
  - name: trainer
    image: huggingface/transformers:latest
    command: ["python", "train.py"]
    resources:
      limits:
        nvidia.com/gpu: 8
        memory: 512Gi
  initContainers:
  - name: verify-tee
    image: phala/attestation-verifier:latest
    command: ["verify-node"]

Auto-Scaling

Horizontal Pod Autoscaling (HPA) for Inference:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: confidential-llm-inference-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: llm-inference
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: nvidia.com/gpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Pods
    pods:
      metric:
        name: requests_per_second
      target:
        type: AverageValue
        averageValue: "1000"

Layer 5: AI Application Patterns

Pattern 1: Confidential LLM Inference

Architecture:

Deployment (Phala Cloud):

version: "3"
services:
  llm-server:
    image: vllm/vllm-openai:latest
    environment:
      - MODEL=meta-llama/Llama-2-70b-chat
      - TENSOR_PARALLEL_SIZE=4
    ports:
      - "8000:8000"
    volumes:
      - model-cache:/root/.cache
      - /var/run/dstack.sock:/var/run/dstack.sock
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 4
              capabilities: [gpu]
volumes:
  model-cache:
    driver: encrypted-volume

API Endpoint:

import openai

def confidential_llm_query(prompt):
    attestation = requests.get("https://llm.phala.network/.well-known/attestation")
    if not verify(attestation):
        raise SecurityError("LLM not in verified TEE!")
    client = openai.OpenAI(api_key="phala-key", base_url="https://llm.phala.network/v1")
    response = client.chat.completions.create(
        model="llama-70b",
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message.content

result = confidential_llm_query("Sensitive medical question")

Monitoring & Observability

Metrics to Track

TEE-Specific Metrics:

  • Attestation:
    • Attestation verification success rate
    • Attestation check latency ms
    • Continuous verification failures
  • Security:
    • Unauthorized access attempts
    • TEE boundary violations
    • Encryption key rotations
  • Performance:
    • GPU TEE overhead percent
    • Memory encryption impact ms
    • Encrypted storage IOPS

Prometheus + Grafana Dashboard:

groups:
- name: confidential-ai-alerts
  rules:
  - alert: AttestationFailure
    expr: attestation_verification_success_rate < 0.99
    for: 5m
    annotations:
      summary: "TEE attestation failing"
      description: "{{ $value }}% of attestations failing"
  - alert: HighGPUTEEOverhead
    expr: gpu_tee_overhead_percent > 20
    for: 10m
    annotations:
      summary: "GPU TEE overhead too high"
      description: "{{ $value }}% overhead (expected <15%)"

Next Steps

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