How to Implement Hard Multi-Tenancy for GPU Infrastructure

Hard multi-tenancy is the architectural principle of providing strict, kernel-level isolation between tenants on shared infrastructure, ensuring no data leakage or performance interference. For GPU clusters in a sovereign AI cloud, this is non-negotiable for hosting competing enterprises or government agencies. Implementation requires a layered approach: physical GPU partitioning with technologies like NVIDIA Multi-Instance GPU (MIG), network segmentation with a service mesh like Istio, and storage quotas via a CSI driver. Each tenant's workloads, from training to inference, must run in fully isolated Kubernetes namespaces with dedicated resource guarantees.

Start by partitioning GPUs using MIG or AMD CDNA's equivalent technology to create virtual GPU instances. Enforce these partitions using the NVIDIA GPU Operator in Kubernetes. Next, implement a zero-trust network model with Calico NetworkPolicy to prevent any cross-tenant communication. Finally, integrate a Keycloak-based identity provider for robust RBAC and audit trails. This creates a platform where tenants operate as if on dedicated hardware, a core requirement for operational sovereignty. For related concepts, see our guide on How to Architect Sovereign AI Cloud Networking and Segmentation.