Confidential LLMs: Privacy-Preserving Large Language Models in Production

TL;DR: GPU Trusted Execution Environments (TEE) enable confidential LLM deployment with cryptographic privacy guarantees. NVIDIA H100/H200 Confidential Computing delivers 95-99% of native performance while protecting model weights, training data, and inference inputs. Deploy on Phala Confidential AI Cloud to eliminate the trade-off between AI capability and privacy with public attestation.

Executive Summary

The LLM Privacy Challenge:

• Large language models process sensitive data (customer support, healthcare, legal, financial)
• Traditional deployment exposes data to cloud provider, model operator, infrastructure admins
• Privacy concerns limit AI adoption: 73% of enterprises cite data security as barrier (Gartner 2024)
• Model IP protection critical: Proprietary model weights worth $50M+ for leading LLMs

The Confidential LLM Solution:

• GPU TEE protects entire inference/training pipeline
• Hardware-enforced isolation: No privileged access to model or data in use
• Remote attestation: Cryptographic proof of privacy protections
• Production-ready performance: <5% overhead on NVIDIA H100/H200 TEE
• Result: Enterprise AI deployment without privacy compromise

Key Benefits:

Understanding Confidential LLMs

What Makes an LLM “Confidential”?

Core requirements:

1. Model weight protection - Proprietary model parameters protected from unauthorized access
2. Input privacy - User prompts never exposed in plaintext
3. Output confidentiality - Generated responses protected until delivery to authorized recipient
4. Training data protection - Fine-tuning datasets encrypted during training
5. Verifiable privacy - Remote attestation proves protections are active
6. No privileged access - Even infrastructure admins cannot access data/model

GPU TEE for LLMs

Why GPUs require TEE:

• Modern LLMs require massive compute and memory resources.
• Example: Llama 3.1 405B with 405 billion parameters needs ~810GB memory and 52 trillion FLOPS/token.
• Solution: Multi-GPU with TEE using 8x NVIDIA H200 for 1.1TB total memory, achieving 95-99% of native performance.

NVIDIA H100/H200 Confidential Computing:

• Hardware encryption: AES-256 for all GPU memory (VRAM, HBM3e)
• Attestation: Cryptographic proof of TEE configuration
• Performance: 95-99% of native throughput (overhead <5%)
• Scale: Multi-GPU TEE (8x H100/H200 for 405B+ models)
• Availability: Phala Cloud (production), Azure (pilot), AWS (2026 roadmap)

The Confidential LLM Stack

## Layer 1: Hardware (NVIDIA H100/H200 TEE)
├─ GPU memory encryption (AES-256)
├─ Secure boot and attestation
├─ NVLink TEE (protected multi-GPU)
└─ Hardware-enforced isolation

## Layer 2: Runtime (Phala Cloud Infrastructure)
├─ TEE initialization and verification
├─ Encrypted model loading
├─ Secure inference orchestration
└─ Attestation generation

## Layer 3: Model Deployment ([Dstack SDK](https://phala.com/dstack))
├─ Containerized model deployment
├─ Automatic TEE configuration
├─ Attestation publication
└─ API endpoint management

## Layer 4: Application Integration
├─ Client libraries (Python, JavaScript, REST)
├─ Attestation verification
├─ End-to-end encryption
└─ Usage monitoring

## Result: Complete privacy stack from hardware to application

Confidential LLM Inference

Architecture Overview

Production Deployment Pattern:

Confidential LLM Inference Flow:

Key Security Properties:

1. Client sends encrypted request → API endpoint (TLS)
2. Request enters GPU TEE (H100/H200)
3. Decryption happens ONLY inside GPU TEE
4. Model inference in encrypted memory (VRAM)
5. Response encrypted before leaving TEE
6. Continuous attestation verification proves TEE integrity

Implementation Example:

from dstack import DstackClient
import requests

class ConfidentialLLM:
    def __init__(self, api_endpoint: str):
        self.endpoint = api_endpoint
        self.dstack = DstackClient()
        attestation = self.dstack.get_attestation(api_endpoint)
        if not self.verify_attestation(attestation):
            raise Exception("TEE attestation verification failed")

    def verify_attestation(self, attestation: dict) -> bool:
        return self.dstack.verify_remote_attestation(
            attestation,
            expected_tee_type="nvidia_h100_cc",
            expected_model_hash="sha256:abc123...",
            min_trust_level="high"
        )

    def generate(self, prompt: str, max_tokens: int = 100) -> str:
        response = requests.post(
            f"{self.endpoint}/generate",
            json={"prompt": prompt, "max_tokens": max_tokens, "temperature": 0.7},
            verify=True
        )
        return response.json()["text"]

llm = ConfidentialLLM("https://my-confidential-llm.phala.cloud")
result = llm.generate("Patient presents with chest pain. Analysis:")

Deployment: Step-by-Step

Deploying Llama 3.1 70B on [Phala Confidential AI Cloud](https://docs.phala.com/phala-cloud/getting-started/overview) (GPU TEE):

from dstack_sdk import PhalaCloud

phala = PhalaCloud(api_key="your_api_key")
deployment = phala.deploy_llm(
    model_name="llama-3.1-70b-instruct",
    model_source="huggingface",
    gpu_type="h100_tee",
    gpu_count=4,
    memory_per_gpu="80GB",
    confidential_computing=True,
    attestation_public=True,
    max_batch_size=16,
    max_sequence_length=8192,
    dtype="bfloat16",
    min_replicas=2,
    max_replicas=10,
    autoscaling_metric="requests_per_second",
    autoscaling_target=100
)
print(f"Deployment ID: {deployment.id}")
print(f"API Endpoint: {deployment.endpoint}")
print(f"Attestation URL: {deployment.attestation_url}")

Step 2: Verify deployment and attestation:

attestation = phala.get_attestation(deployment.id)
print(f"TEE Type: {attestation['tee_type']}")
print(f"GPU Count: {attestation['gpu_count']}")
print(f"Model Hash: {attestation['model_hash']}")
print(f"Trust Level: {attestation['trust_level']}")

Step 3: Production inference:

import openai

openai.api_base = deployment.endpoint
openai.api_key = "your_deployment_key"

response = openai.ChatCompletion.create(
    model="llama-3.1-70b-instruct",
    messages=[
        {"role": "system", "content": "You are a medical AI assistant. Patient data is confidential."},
        {"role": "user", "content": "Patient: 45F, diabetes, presents with severe headache. Differential diagnosis?"}
    ],
    max_tokens=500
)
print(response.choices[0].message.content)

Conclusion

Key Takeaways

Confidential LLMs enable previously impossible deployments:

1. Healthcare AI: Process patient data with HIPAA compliance
2. Financial AI: Protect proprietary trading models in cloud
3. Legal AI: Maintain attorney-client privilege with TEE isolation
4. Government AI: Classified intelligence analysis in secure cloud
5. Enterprise AI: Customer data analytics with privacy guarantees

Technology readiness (2025):

• GPU TEE production-ready (NVIDIA H100/H200)
• Performance overhead: <5%
• Deployment simplified (Dstack SDK)
• Public attestation enables verification
• Market adoption accelerating

Bottom line: Confidential computing resolves the AI privacy paradox. Hardware-enforced protection enables deployment of powerful AI on sensitive data without privacy compromise. 2025 marks the transition from “too risky to deploy” to “how fast can we deploy?”

Related Resources

• Phala Homepage
• Learning Hub
• Phala Cloud Documentation
• Dstack Documentation
• Success Stories

Next Steps

• Get Started with Phala Cloud
• Deploy Your First Confidential VM
• Explore Confidential AI
• Join Our Startup Program
• Contact Us