How to Prove AI Compliance on Marvell Custom Silicon Infrastructure

1. What is Marvell Custom Silicon

Marvell Technology designs and manufactures the connectivity and custom compute silicon that allows AI data centers to function as unified inference systems. While NVIDIA provides the GPU compute, Marvell provides the fabric that connects those GPUs together and to the outside world.

Key technologies

• Custom ASICs: Application-specific integrated circuits designed for individual hyperscaler customers. Marvell builds the silicon behind Amazon Trainium (custom AI training chips), Microsoft Maia (custom AI accelerator), and Meta's data processing units. 18 cloud-provider design wins, $1.5 billion annual run rate.
• 1.6T Optical Interconnect: Terabit-class optical links that carry data between GPU clusters. The speed and reliability of these links directly affects inference latency, throughput, and fault tolerance. Marvell's optical PHYs and DSPs are the physical layer of AI data center networking.
• NVLink Fusion: Partnership with NVIDIA connecting Marvell's custom silicon and networking to the NVIDIA AI ecosystem, enabling GPU-to-custom-ASIC communication at NVLink speeds rather than PCIe speeds.
• Teralynx Ethernet Switching: Data center switches that route inference traffic between compute nodes. Marvell's switching silicon handles the east-west traffic that multi-model and multi-agent AI systems generate.
• Data Processing Units (DPUs): Offload networking, security, and storage functions from CPUs, freeing compute for AI workloads. DPUs handle encryption, firewalling, and traffic shaping at line rate.

2. The Compliance Gap

Regulatory frameworks require evidence of AI system behavior at every layer of the stack. Today, most compliance evidence covers the application layer (model inputs and outputs) and sometimes the compute layer (GPU utilization, model versioning). The interconnect layer -- the silicon that connects everything -- has zero compliance evidence.

This creates specific regulatory gaps:

The interconnect layer is the largest unwitnessed surface in modern AI infrastructure. Every model call, every agent action, every tool invocation passes through this silicon. Without compliance evidence at this layer, the audit trail has a gap that regulators and auditors will eventually find.

3. SWT3 Procedure Mapping

The following SWT3 procedures map directly to Marvell custom silicon infrastructure components. Each procedure produces a cryptographic witness anchor that can be independently verified.

4. Full Stack Coverage: NVIDIA + Marvell + Application

A complete AI inference stack has three layers. SWT3 provides compliance evidence at every layer:

Most compliance tools only cover the application layer. Some cover compute. SWT3 is the only protocol that provides cryptographic evidence across all three layers, from the model API call through the GPU compute through the Marvell interconnect fabric.

For NVIDIA compute layer coverage, see the AI Factory Compliance Blueprint. For NVIDIA edge deployments, see the N1X Local AI Agent Governance guide.

5. Implementation

SWT3 witnesses hardware infrastructure through the runtime_profile configuration in .swt3.yaml. This allows the SDK to capture hardware topology and validate it against expected configurations without requiring direct silicon-level integration.

# .swt3.yaml -- Marvell infrastructure profile
version: "1"
tenant_id: "YOUR_TENANT"
clearing_level: 1
signing_algorithm: "hmac-sha256"

runtime_profile:
  expected_topology: "marvell-optical-fabric"
  expected_accelerator: "custom-asic"
  min_gpu_count: 8
  min_memory_mb: 81920

  # Marvell-specific attestation context
  # (captured in AI-HW.1 witness anchors)
  custom_fields:
    interconnect_type: "1.6T-optical"
    switch_family: "teralynx"
    dpu_encryption: "aes-256-gcm"
    fabric_redundancy: "dual-rail"
    asic_firmware_hash: "sha256:a1b2c3..."

procedures:
  - AI-HW.1    # Hardware attestation
  - AI-PERF.1  # Interconnect performance
  - AI-SUPPLY.1 # Silicon supply chain
  - AI-ROBUST.1 # Fabric fault tolerance
  - AI-CYBER.1 # DPU security controls
  - AI-INF.1   # Inference provenance
  - AI-CHAIN.1 # Multi-chip chain of custody

The SDK reads this configuration and includes hardware context in every witness anchor. Auditors can verify that the declared topology matches the actual infrastructure by comparing the runtime_profile hash in the anchor against the expected configuration.

Witnessing interconnect performance

# Python: Witness interconnect performance metrics
from swt3_ai import SWT3Witness

witness = SWT3Witness(
    endpoint="https://sovereign.tenova.io/api/v1/witness",
    api_key="axm_live_...",
    tenant_id="YOUR_TENANT",
    clearing_level=1,
)

# Record interconnect performance baseline
witness.witness_performance(
    metrics_evaluated=5,
    metrics_passing=5,
    benchmark_type="custom",       # interconnect-specific
    context={
        "interconnect_latency_p50_us": 12.4,
        "interconnect_latency_p99_us": 45.2,
        "optical_link_bandwidth_gbps": 1600,
        "switch_throughput_mpps": 12800,
        "error_rate_per_billion": 0.3,
    },
)

# Record supply chain provenance for custom ASIC
witness.witness_supply_chain_risk(
    suppliers_assessed=3,
    suppliers_compliant=3,
    risk_level="low",
    context={
        "asic_fab": "tsmc-2nm",
        "silicon_revision": "B0",
        "firmware_version": "4.2.1",
        "last_audit_date": "2026-05-15",
    },
)

6. Regulatory Requirements

The following regulatory frameworks impose obligations that extend to the interconnect layer of AI infrastructure:

7. Industry Relevance

Hyperscalers

Amazon, Microsoft, Meta, and Google all commission Marvell custom silicon for their AI infrastructure. Each operates under different regulatory regimes (FedRAMP for government workloads, GDPR for European customers, SOC 2 for enterprise SLAs). SWT3 provides a single evidence framework that covers the Marvell hardware layer regardless of which regulatory mapping the customer requires.

Sovereign cloud operators

Nations building sovereign AI infrastructure using Marvell silicon need compliance evidence that stays within their jurisdiction. SWT3 clearing levels ensure that hardware attestation evidence can be produced at Level 3 (Classified) without exposing topology details, silicon revisions, or performance baselines to external parties.

Defense and government (CMMC)

Defense contractors deploying AI on Marvell-powered infrastructure must satisfy CMMC supply chain requirements (SR family). AI-SUPPLY.1 provides the evidence chain from silicon fab to deployed firmware. Combined with AI-CYBER.1 for DPU security controls, this covers the hardware layer that CMMC assessors increasingly examine.

Financial services (SR 11-7)

Model risk management under SR 11-7 requires that infrastructure-layer performance be documented as a factor in model outcomes. If interconnect latency affects inference latency, and inference latency affects model performance, then the Marvell fabric is part of the model risk surface. AI-PERF.1 captures this evidence.