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.
Guide
How to Implement Hard Multi-Tenancy for GPU Infrastructure
A technical guide to building secure, isolated environments for multiple tenants on shared GPU clusters using kernel-level isolation, network segmentation, and storage controls.
This guide details the technical implementation of hard multi-tenancy to securely isolate AI workloads from different tenants on shared GPU clusters.
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.
HARDWARE VS. SOFTWARE VS. HYBRID
GPU Isolation Technology Comparison
A technical comparison of core technologies for implementing kernel-level GPU isolation in hard multi-tenancy architectures.
| Isolation Feature | NVIDIA Multi-Instance GPU (MIG) | AMD CDNA Multi-Process Service (MPS) | Kubernetes Device Plugins with Time-Slicing |
|---|---|---|---|
Isolation Granularity | Hardware-enforced partitions (GPU instances) | Process-level with memory protection | Software-based time-sharing of entire GPU |
Memory Protection | |||
Compute Protection | |||
Fault Isolation | Instance crash does not affect others | Process crash contained by MPS | Fault can crash all co-located workloads |
Performance Predictability | Guaranteed, dedicated resources | High, with managed contention | Low, subject to noisy neighbor effects |
Maximum Tenants per A100/H100 | 7 | Limited by VRAM, typically 4-8 | Unlimited, but with severe degradation |
Management Overhead | High (requires GPU reconfiguration) | Medium (requires MPS daemon management) | Low (managed by Kubernetes scheduler) |
Ideal Use Case | Strictly regulated environments, guaranteed SLAs | High-performance computing, shared research clusters | Development, testing, low-risk batch inference |
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Common Mistakes
Implementing hard multi-tenancy for GPU infrastructure is critical for secure, sovereign AI clouds. These are the most frequent and costly errors teams make when architecting isolation for government or high-security enterprise tenants.
Soft multi-tenancy relies on software-level isolation (namespaces, cgroups) which shares a single host kernel. This creates a broad attack surface where a kernel exploit can lead to cross-tenant data leakage. For sovereign AI workloads involving classified data or competing enterprises, this risk is unacceptable.
Hard multi-tenancy requires kernel-level isolation, where each tenant's workload runs on a physically or logically partitioned environment with its own dedicated kernel, such as via NVIDIA Multi-Instance GPU (MIG) or full-stack virtualization with AMD SEV or Intel TDX. This ensures that a compromise in one tenant's environment cannot propagate to others, meeting the 'territorial and operational' sovereignty requirements detailed in our guide on How to Build a Sovereign AI Cloud from the Ground Up.
About the author
Prasad Kumkar
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
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