How to Maintain Compliance When AI Workloads Span NVIDIA, Google TPU, and AMD Silicon

1. The Cross-Silicon Compliance Gap

When an enterprise runs its fraud detection model on an NVIDIA H100 cluster in Virginia and its customer service model on Google TPUs in a third-party data center in New York, it faces a question no single vendor can answer: "Can you prove, to a regulator, that both deployments maintained continuous compliance?"

NVIDIA's tooling monitors NVIDIA clusters. Google's tooling monitors Google TPU clusters. Neither can attest what happened on the other's silicon. The enterprise is left assembling compliance evidence from two incompatible telemetry stacks, two different log formats, and two vendor consoles that require separate authentication.

This gap widens as silicon diversity increases. AMD MI300X offers compelling price-performance for inference. AWS Trainium is cost-optimized for training. Intel Gaudi targets inference throughput. Organizations that optimize across these architectures gain economic advantage but lose compliance coherence.

SWT3 solves this by operating at the application layer, above the silicon. The fingerprint formula produces identical cryptographic attestations regardless of which accelerator processes the inference. An auditor verifying an anchor cannot tell from the fingerprint whether it was minted on an NVIDIA GPU, a Google TPU, or an AMD MI300X. The ai_context records which silicon was present, but the cryptographic proof is vendor-neutral by design.

2. Multi-Vendor Silicon Landscape

All discovery is automatic. The SDK tries each path in priority order and returns the first successful detection. No configuration required. No vendor-specific dependencies. If the vendor's CLI tools are present on the host, the SDK finds them. If not, it falls through to the next path gracefully.

The Fragmentation Accelerates

In a single week (June 24-25, 2026), OpenAI unveiled Jalapeno -- a custom inference ASIC built with Broadcom, targeting 50% lower cost-per-token than NVIDIA GPUs -- and IBM announced the world's first sub-1nm transistor architecture. Enterprises will increasingly run mixed fleets: NVIDIA for training, custom silicon for inference, cloud-native accelerators for burst. Each vendor has a commercial interest in their workloads appearing compliant. Independent cross-silicon attestation is the only architecture that scales without conflicts of interest.

3. How SWT3 Hardware Discovery Works

When you call witness.witness_hardware(), the SDK executes a priority-ordered discovery chain:

1. NVIDIA -- Tries pynvml (structured API, optional dependency), then nvidia-smi subprocess
2. Google TPU -- Tries JAX device enumeration (Python) or TPU_NAME environment variable (Node.js)
3. AMD -- Tries rocm-smi subprocess with CSV output
4. AWS -- Tries neuron-ls subprocess with JSON output
5. Intel -- Tries hl-smi subprocess with CSV output
6. PCI fallback -- Scans /sys/bus/pci/devices for accelerator class codes (0x0302, 0x1200)

The first path that returns results wins. All hardware identifiers (UUIDs, bus IDs, serial numbers) are SHA-256 hashed at discovery time. Raw values never leave the discovery module.

3.1 What Gets Recorded

The witness anchor's ai_context (at clearing level 0 or 1) records:

• silicon_vendor -- which vendor's silicon was detected (nvidia, google, amd, aws, intel, mixed)
• discovery_method -- which tool produced the data (pynvml, nvidia-smi, jax, rocm-smi, neuron-ls, hl-smi, tpu-env, pci)
• topology -- cluster topology (single, multi-gpu, DGX-H100, NVL72, multi-node)
• accelerator_count -- how many accelerator units were detected
• accelerators[] -- per-unit details (name, memory, vendor, family, hashed IDs)

At clearing level 2 and above, the context is stripped (sensitive clearing). The fingerprint itself contains the accelerator count and topology code but not the vendor identity, preserving the vendor-neutral verification property.

4. Regulatory Mapping

5. Multi-Silicon Profile Quick Start

Create a .swt3.yaml file in your project root:

profile: multi-silicon
api_key_env: SWT3_API_KEY
tenant_id: YOUR_TENANT
agent_id: inference-router-v1

The multi-silicon profile activates 8 required procedures covering hardware attestation, inference provenance, drift detection, environment attestation, performance validation, supply chain risk, model integrity, and audit logging. Hardware attestation is required and re-attests every 30 minutes.

Then witness hardware at service startup:

# Python
from swt3_ai import Witness

witness = Witness.from_config()
witness.witness_hardware()  # Auto-detects any accelerator vendor

// TypeScript
import { Witness } from "@tenova/swt3-ai";

const witness = Witness.fromConfig();
witness.witnessHardware();  // Auto-detects any accelerator vendor

That is the entire integration. No vendor-specific configuration. No conditional imports. The SDK discovers whatever silicon is present and records it in the witness anchor.

hardware:
  require_attestation: true
  runtime_profile:
    expected_silicon_vendor: nvidia
    min_gpu_count: 8
    expected_topology: DGX-H100

6. Cross-Silicon Drift Detection

When a model migrates from one silicon architecture to another, its behavior may change. Floating point precision differs between CUDA cores and TPU matrix units. Quantization kernels produce different rounding. Attention implementations vary across vendor libraries.

These differences are typically small but can be compliance-relevant for high-risk AI systems where output reproducibility matters (EU AI Act Art. 15).

SWT3 detects silicon migration through the hardware attestation chain:

1. AI-HW.1 records the silicon vendor at service startup
2. AI-DRIFT.1 monitors model output for statistical drift over time
3. If a deployment's silicon vendor changes between attestation windows, the anchor chain shows the transition: the silicon_vendor field changes from one anchor to the next
4. Any concurrent drift in model outputs (detected by AI-DRIFT.1) can be correlated to the silicon change

An auditor reviewing the anchor chain can see: "Model X was running on NVIDIA H100 from January through March (stable drift metrics), migrated to Google TPU v5p in April (drift spike), then stabilized on TPU by May." The evidence trail is automatic.

7. Factor Encoding Reference

The AI-HW.1 procedure encodes three factors into the witness fingerprint:

The silicon vendor is not encoded in the factors. This is intentional: the fingerprint is vendor-neutral, meaning a verifier can confirm the attestation without knowing which vendor's silicon produced it. The vendor identity is recorded in the ai_context observations at clearing level 0-1 for auditors who need the detail.

8. References

• Regulation (EU) 2024/1689 -- EU AI Act, Art. 12 (automatic logging), Art. 15 (accuracy and robustness)
• EU AI Act Omnibus Agreement (May 7, 2026) -- timeline changes for high-risk systems
• NIST AI Risk Management Framework 1.0 -- MG-3.2 (resource allocation monitoring)
• Executive Order 14110 -- Sec. 4.2 (compute reporting thresholds)

Related SWT3 Guides

• EU AI Act Omnibus Agreement -- comprehensive analysis of all Omnibus amendments
• Bias Detection Witnessing -- sensitive data processing for bias correction
• AI Factory Compliance Blueprint -- GPU cluster metering with SWT3
• SWT3 Protocol Specification -- formal specification with ABNF grammar
• Design Rationale -- why every protocol decision was made
• Live Demo Audit Portal -- interactive compliance evidence