GPU Multi-Tenancy Without Security Theater: Isolation, Quotas, Noisy Neighbors, and Confidential Computing

This article provides a deep technical analysis of GPU multi-tenancy in enterprise AI platforms, focusing on the security and isolation challenges beyond basic scheduling. It examines various GPU sharing mechanisms such as Multi-Instance GPU (MIG), time-slicing, virtual GPUs (vGPU), and whole-GPU allocation, highlighting their respective isolation properties, performance implications, and security boundaries. The article emphasizes that effective GPU governance requires a layered approach combining Kubernetes namespaces, RBAC, quotas, priority scheduling, and confidential computing with attestation and encryption. It warns against treating any single feature as a complete security solution and advocates for a threat-model-driven architecture. The content is highly relevant to IT/technology professionals involved in AI infrastructure, cloud-native computing, and platform engineering.