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Overview

Confidential Compute VMs on io.net provides hardware-based encryption that protects your AI data, model weights, and computations from unauthorized access, including cloud operators, administrators, or malicious actors. Unlike traditional cloud environments that only encrypt data at rest and in transit, Confidential Compute maintains end-to-end encryption even in memory while your workloads are running. This capability is powered by Intel Trust Domain Extensions (TDX) on 5th and 6th generation Intel Xeon processors, combined with io.net’s decentralized GPU network.

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What Is Confidential Computing?

Confidential Computing is a hardware-based security model that ensures your data stays encrypted even while being processed. Using Trusted Execution Environments (TEEs), it creates isolated regions of memory where code and data remain protected from all external access, including the host OS, hypervisor, and cloud administrators.

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Key Technology

  • Intel Trust Domain Extensions (TDX): Provides per-VM encryption and isolation for workloads.
  • Trusted Execution Environments: Secure enclaves ensure no unauthorized party can read memory contents.
  • Hardware Attestation: Verifies the integrity of BIOS, firmware, and kernel before execution.
io.net delivers the same enterprise-grade protection as Azure and Google Cloud Confidential VMs, at a fraction of the cost, with bare-metal GPU performance and no centralized dependency.

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Why It Matters

When training or running AI models on traditional hyperscalers, your data sits unencrypted in memory where it can be exposed through:
  • Insider threats or compromised infrastructure operators
  • Cyberattacks targeting shared cloud environments
  • Government subpoenas or data access requests
These risks endanger:
  • Proprietary model architectures
  • Customer PII, health records, and financial data
  • Sensitive inference requests revealing business logic
Big Tech encrypts data at rest and in transit, but not during processing, when your most sensitive data is exposed.

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Key Benefits

  • Protect Intellectual Property: Encrypt training data, model weights, and architectures during AI model development.
  • Enable Secure Collaboration: Support multi-party or federated learning while keeping datasets isolated and private.
  • Ensure Regulatory Compliance: Meet HIPAA, GDPR, and financial data requirements with full auditability.
  • Secure Inference at Scale: Protect live prompts, responses, and intermediate model states.
  • Reduce Costs: Run on a decentralized GPU infrastructure, up to 90% cheaper than hyperscalers.

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Getting Started

Confidential Compute is available upon request through io.net’s managed services, supporting machines from H100 up to B200. To begin using Confidential Compute on io.net:
  1. Request Access: Submit a provisioning request through the io.cloud Confidential Compute Form. Choose your preferred GPU type (H100, H200, or B200) and provide basic project details.
    Our specialists will reach out to understand your needs and assist with configuring the machines to your specifications.
  2. Deploy a Confidential VM: Once set up, deploy your Confidential VM using the web console, CLI, or API.
  3. Verify Attestation: Before running sensitive workloads, verify your instance’s attestation report to confirm the integrity of its BIOS, firmware, and TDX configuration. This ensures your VM is operating in a trusted, secure state.
  4. Integrate Seamlessly: Run workloads on Ubuntu 24.04 (LTS) with built-in Intel TDX support. Existing MLOps pipelines run without modification. You can migrate existing workflows directly to io.net without changing your training or deployment code.
  5. Run Secure Workloads: Connect to your instance and deploy your AI training or inference workloads as usual. All computations, model weights, and data remain encrypted in memory throughout processing, maintaining full performance and GPU acceleration.
Performance impact is minimal and with full GPU capabilities preserved. This validates secure AI pipelines on cost-effective infrastructure.

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Compliance and Use Cases

Confidential Compute supports secure operations across regulated and high-risk industries:
  • Healthcare: Train on PHI and diagnostic data securely.
  • Finance: Protect trading models and risk analytics pipelines.
  • Legal: Safeguard sensitive inference workloads and document data.
  • Defense and Government: Maintain sovereignty over AI models and datasets.
Additionally, all workloads comply with:
  • HIPAA (Health Insurance Portability and Accountability Act)
  • GDPR (General Data Protection Regulation)
  • PCI DSS (Payment Card Industry Data Security Standard)