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Deploying Proprietary Models Securely with NVIDIA Confidential Computing on Self-Hosted Kubernetes - Home Deploying Proprietary Models Securely with NVIDIA Confidential Computing on Self-Hosted Kubernetes - Home

Deploying Proprietary Models Securely with NVIDIA Confidential Computing on Self-Hosted Kubernetes

Deploying Proprietary Models Securely with NVIDIA Confidential Computing on Self-Hosted Kubernetes - Home Deploying Proprietary Models Securely with NVIDIA Confidential Computing on Self-Hosted Kubernetes - Home

Deploying Proprietary Models Securely with NVIDIA Confidential Computing on Self-Hosted Kubernetes

Table of Contents

Deploying Proprietary Models Securely with NVIDIA Confidential Computing on Self-Hosted Kubernetes

  • Introduction
  • Scope
  • Primary Roles
  • Architecture Summary
  • Trust & Threat Model
  • Required Capabilities
  • Platform and Hardware Requirements
  • Workload and Model Lifecycle
  • Attestation and Key-Release Flow
  • Network and Service Integration
  • Operations and Failure Handling
  • Sizing and Performance
  • Reference Implementations
  • Deployment Pattern
  • Limitations and Open Questions
  • Conclusion

Appendix

  • Appendix A. Validated Configuration Profile Template
  • Appendix B. Sample Ownership Matrix
  • Appendix C. Component Responsibilities and Interfaces
  • Appendix D. Lifecycle and Change Control
  • Appendix E. Failure Modes and Acceptance Tests
  • Appendix F. Deployment Detail Checklist

Notices

  • Notices
  • Limitations and Open Questions

Limitations and Open Questions#

The deployment of workloads in a Confidential Containers architecture has certain limitations pertaining to the container runtime choices, K8S node configurations and runtime support. Please refer to this document for the current known limitations: https://docs.nvidia.com/datacenter/cloud-native/confidential-containers/1.0.0/overview.html#limitations-and-restrictions.

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Deployment Pattern

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Conclusion

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Last updated on Jun 01, 2026.