Deployment Pattern

A typical deployment places a confidential GPU node pool adjacent to an enterprise AI platform. Each node has a TEE-capable CPU, CC-capable GPUs, supported firmware, and a Kubernetes configuration that can launch confidential pod sandboxes.

The model provider supplies the signed workload image, encrypted model artifacts, image digest and signing metadata, key-release policy, inference-server certificate requirements, and logging requirements.

The CC software provider supplies the confidential runtime, attestation components, key-release integration, measurement tooling, supported configuration, and failure signals.

The platform operator supplies the Kubernetes cluster, worker nodes, GPU Operator path, RuntimeClass, node labels, network policy, DNS, routes or load balancers, registry access, SIEM integration, and monitoring that excludes payloads.

The security team reviews attestation policy, reference-value governance, KMS/HSM governance, SIEM integration, incident response, and change control.

This isn’t an install guide. The final deployment guide will include version pins, manifests, RuntimeClass configuration, key-release configuration, policy examples, reference-value registration, validation commands, failure-mode tests, and ownership decisions. The full checklist is in Appendix F.