NVIDIA TEE Implementation

Overview

Enclava leverages NVIDIA H100 GPUs with confidential computing capabilities for secure AI inference. This provides hardware-level protection for sensitive workloads while maintaining high performance.

Key Features

Hardware Security

• Memory Encryption: AES-256-GCM encryption for all GPU memory (HBM3, L2 cache, registers)
• Secure Boot: Hardware root of trust with cryptographic verification
• PCIe Protection: Encrypted communication between CPU and GPU

Multi-Instance GPU (MIG)

• Up to 7 isolated partitions per H100 GPU
• Independent encryption keys per partition
• Hardware-enforced resource isolation (memory, compute, bandwidth)

Remote Attestation

• Cryptographic proof of authentic NVIDIA hardware
• Verification of security configuration before workload deployment
• Chain of trust from hardware boot to application

Security Model

Protection Level

Threat	Mitigation Status
Cloud Provider Access	Strong - Hardware encryption prevents access
Host OS Compromise	Strong - TEE isolation from host
Physical Tampering	Strong - Memory encryption and secure boot
Side Channels	Partial - Some timing channels remain
Network Attacks	Strong - End-to-end encryption

Performance Impact

• Memory encryption: < 5% overhead
• Attestation: One-time setup cost
• Overall inference: Minimal impact

Integration with Enclava

1. Workload Submission: Client sends encrypted request to Enclava
2. Attestation: Platform verifies GPU TEE before processing
3. Secure Processing: Model inference runs in isolated MIG partition
4. Encrypted Response: Results returned without exposing data

External Resources

• NVIDIA H100 Datasheet
• NVIDIA Confidential Computing Documentation
• NVIDIA GPU Operator for Kubernetes

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For threat analysis details, see NVIDIA Threat Model.