I. Converged Trust Fabric: Extending HyperBEAM's TEE to GPU Compute Layers

In the AO ecosystem, determinism and verifiability form the cornerstone of decentralized computing networks. At its foundation lies hardware-backed Trusted Execution Environments (TEE), where AO already implements AMD SEV-SNP attestation through HyperBEAM's dev_snp.erl device. This mechanism enables any participant to cryptographically verify execution integrity via:

%% Generate Attestation Report
{ok, JsonReport} = dev_snp_nif:generate_attestation_report(UniqueDataBinary, VMPL)
%% Verify Attestation Report
{ok, pass} = dev_snp_nif:verify_measurement(Report, ExpectedMeasurement)

These NIF bindings to AMD's SEV-SNP Rust crate establish a root-of-trust for CPU computations through firmware-signed attestation reports and measurement validation.

When extending this paradigm to GPU workloads, new verification challenges emerge. Unlike CPU TEEs that can directly leverage processor security features, GPU computations require specialized extensions. This is where APUS Network's GPU TEE integration becomes critical, implementing triple guarantees through NVIDIA's security stack:

1. Immutable Execution Contexts: Hardware-enforced isolation of CUDA kernels mirrors AMD SEV's memory encryption, preventing runtime tampering during GPU task processing.

2. Deterministic Proof Chains: Combines NVIDIA's CUDA-Determinism tools with TEE measurement extensions, creating cryptographic proof of consistent input-output mapping across decentralized GPU nodes.

3. Attestation-Driven Economics: APUS bridges GPU TEE evidence with AO's attestation, applying financial consequences for nodes failing attestation checks.

By layering GPU-specific TEE mechanisms atop AO's established CPU verification framework, APUS enables seamless scaling of AI workloads while preserving the network's core security invariants from silicon to protocol layer.

II. NVIDIA Confidential Computing Architecture

NVIDIA's hardware-rooted confidential computing architecture extends trust chains between H100 GPUs and CPU TEEs (AMD SEV-SNP/Intel TDX) through IETF RATS-based encrypted pipelines.

1. Encrypted Execution Units

TEE-secured CUDA execution via:

• Cryptographically signed Fatbin containers (compiled with CUDA Toolkit 12.4)

• AES-GCM encrypted PCIe command streams decrypted by GPU HSM

2. Heterogeneous Trust Coordination

CPU-GPU mutual attestation protocol:

1. Composite Attestation: CPU attestation key signs GPU device identity certificates

2. Secure Data Pipeline: Encrypted bounce buffers transmit data from CPU TEE to GPU HBM via NVIDIA drivers

3. Layered Attestation Model

Three hardware-backed verification modes:

• Local GPU Verifier: Validates hardware root-of-trust metrics onsite

• OCSP Protocol: Checks certificate revocation status via NVIDIA online services

• RIM Validation: Matches firmware fingerprints against reference measurements

III. Nvtrust Attestation Implementation Guide

Hardware & Dependencies Requirements

• Supported GPUs: NVIDIA Hopper/Ampere architectures (A100/H100) with TME extensions, persistent mode enabled

• Driver Stack: nvidia-persistenced daemon active, verified via nvidia-smi

• Core SDK: Install attestation SDK (includes Local GPU Verifier)

• Service Prerequisites: Confirm operational status of NVIDIA RIM/OCSP/NRAS services

Attestation Workflow

# Import nvtrust
from nv_attestation_sdk import attestation  
# Step 1: Client initialization  
client = attestation.Attestation("node_id")  
# Step 2: Hybrid verification setup
client.add_verifier(  
    attestation.Devices.GPU,  
    attestation.Environment.LOCAL,  
    "",   # Remote service URL placeholder  
    ""    # OCSP/RIM endpoint placeholder  
)  
# Step 3: Generate & validate evidence chain
attestation_result = client.attest()
validation = client.validate_token('{"x-nv-gpu-attestation-report-available":true}')

This process provides AO with cryptographic proofs confirming GPU environment integrity.

References

[1] = Confidential Computing on NVIDIA H100 GPUs for Secure and Trustworthy AI | NVIDIA Technical Blog

[2] = Unlocking the potential of Privacy-Preserving AI with Azure Confidential Computing on NVIDIA H100 | Microsoft Community Hub

[3] = Overview — NVIDIA Attestation Service 1.0 documentation

[4] = NVIDIA/nvtrust: Ancillary open source software to support confidential computing on NVIDIA GPUs

[5] = docs.nvidia.com/cc-deployment-guide-snp.pdf