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Apus NetworkHow to Implement GPU-Based LLM Inference in AO
With the rapid development of artificial intelligence (AI) technology, an increasing number of large language model (LLM) applications require efficient computational resources. In this article, we will explore how to integrate APUS's GPU extension into the Application Overlay (AO) system to support more powerful AI model inference. Before delving into how GPU extensions work in the AO network, let's briefly review how typical AI applications operate and the composition of the AO ne...
Apus NetworkGetting Started with HyperBEAM: Building a Custom Device for Beginners
AbstractThis guide introduces developers to HyperBEAM's distributed computing framework through hands-on device extension. Learn how to leverage Erlang/OTP architecture and the Converge Protocol to create custom devices. Beginners will gain practical experience through a calculator device demo, understanding NIFs (Native Implemented Functions) and WASM port communication patterns.ChaptersIntroduction to HyperBEAMConverge Protocol : the root of device call logic and pathBuilding a Simple ...
Apus NetworkThe Future Is Deterministic: HyperBeam Architecture and the Importance of Hashpaths in AO
1. IntroductionAs decentralized computation evolves, HyperBeam emerges as a powerful client implementation of the AO-Core protocol, enabling distributed computation in a modular and verifiable way. By abstracting hardware resources and standardizing computation through devices, HyperBeam allows a wide range of computational models to operate seamlessly within the AO ecosystem. At the core of this system lies the concept of Hashpaths, which serve as unique identifiers for computational state a...
Apus NetworkHow to Implement GPU-Based LLM Inference in AO
With the rapid development of artificial intelligence (AI) technology, an increasing number of large language model (LLM) applications require efficient computational resources. In this article, we will explore how to integrate APUS's GPU extension into the Application Overlay (AO) system to support more powerful AI model inference. Before delving into how GPU extensions work in the AO network, let's briefly review how typical AI applications operate and the composition of the AO ne...
Apus NetworkGetting Started with HyperBEAM: Building a Custom Device for Beginners
AbstractThis guide introduces developers to HyperBEAM's distributed computing framework through hands-on device extension. Learn how to leverage Erlang/OTP architecture and the Converge Protocol to create custom devices. Beginners will gain practical experience through a calculator device demo, understanding NIFs (Native Implemented Functions) and WASM port communication patterns.ChaptersIntroduction to HyperBEAMConverge Protocol : the root of device call logic and pathBuilding a Simple ...
Apus NetworkThe Future Is Deterministic: HyperBeam Architecture and the Importance of Hashpaths in AO
1. IntroductionAs decentralized computation evolves, HyperBeam emerges as a powerful client implementation of the AO-Core protocol, enabling distributed computation in a modular and verifiable way. By abstracting hardware resources and standardizing computation through devices, HyperBeam allows a wide range of computational models to operate seamlessly within the AO ecosystem. At the core of this system lies the concept of Hashpaths, which serve as unique identifiers for computational state a...
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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:
Immutable Execution Contexts: Hardware-enforced isolation of CUDA kernels mirrors AMD SEV's memory encryption, preventing runtime tampering during GPU task processing.
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.
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.
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.
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
CPU-GPU mutual attestation protocol:
Composite Attestation: CPU attestation key signs GPU device identity certificates
Secure Data Pipeline: Encrypted bounce buffers transmit data from CPU TEE to GPU HBM via NVIDIA drivers
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
!image.png
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
# 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.
[1] = Confidential Computing on NVIDIA H100 GPUs for Secure and Trustworthy AI | NVIDIA Technical Blog
[3] = Overview — NVIDIA Attestation Service 1.0 documentation
[4] = NVIDIA/nvtrust: Ancillary open source software to support confidential computing on NVIDIA GPUs
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:
Immutable Execution Contexts: Hardware-enforced isolation of CUDA kernels mirrors AMD SEV's memory encryption, preventing runtime tampering during GPU task processing.
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.
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.
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.
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
CPU-GPU mutual attestation protocol:
Composite Attestation: CPU attestation key signs GPU device identity certificates
Secure Data Pipeline: Encrypted bounce buffers transmit data from CPU TEE to GPU HBM via NVIDIA drivers
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
!image.png
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
# 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.
[1] = Confidential Computing on NVIDIA H100 GPUs for Secure and Trustworthy AI | NVIDIA Technical Blog
[3] = Overview — NVIDIA Attestation Service 1.0 documentation
[4] = NVIDIA/nvtrust: Ancillary open source software to support confidential computing on NVIDIA GPUs
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