GPU MULTI-TENANCY ISOLATION MODELS
GPU multi-tenancy isolation operates at four levels. Full GPU exclusive access provides strongest isolation but lowest utilization at 30-40 percent. NVIDIA MIG partitions A100/H100 into up to 7 GPU instances with hardware-enforced memory and cache isolation, achieving 60-75 percent utilization while preventing cross-tenant data leakage. Time-slicing with memory protection offers 70-85 percent utilization but weaker isolation with shared GPU memory bandwidth.
Confidential computing on GPUs is emerging. NVIDIA H100 with confidential compute mode encrypts GPU memory using AES-256-GCM with per-tenant keys managed by the CPU trusted execution environment. Memory encryption introduces 3-8 percent performance overhead. AWS Nitro Enclaves with GPU attachments provide an alternative for regulated workloads, supporting C5 and C6 instance families.
| Isolation Model | Memory Isolation | Cache Isolation | Bandwidth Isolation | Max Tenants/GPU | Overhead |
|---|---|---|---|---|---|
| Full GPU exclusive | Full | Full | Full | 1 | 0% |
| MIG (H100) | Hardware | Hardware | Hardware | 7 | 1-3% |
| MPS (A100) | None | Shared | Shared | 64 | 5-15% |
| Time-slicing | None | Shared | Shared | NVIDIA DM | < 5% |
| Confidential computing | Encrypted | Encrypted | Full (per MIG) | 7 | 3-8% |
GPU MEMORY PROTECTION AND DATA SANITIZATION
GPU memory is not automatically zeroed on job completion. A CUDA kernel may leave residual data in GPU VRAM, exposing previous tenant weights, activations, or inference inputs. NVIDIA GPU memory can retain data for up to 60 seconds after job completion. GPU sanitization requires explicit cudaMemset or cuMemSetAccess operations before releasing memory back to the pool.
At cluster scale, systematic sanitization is essential. A job scheduler must trigger memory wipe on GPU job completion before reallocation. Testing with CUDA-MEMCHECK shows that 12-18 percent of released GPU memory blocks contain residual data. Automated sanitization reduces this below 0.5 percent. Monthly security audits should verify sanitization through randomized memory inspection.
NETWORK SEGMENTATION FOR GPU CLUSTERS
GPU cluster networks require segmentation into three planes. The data plane carries GPU-to-GPU communication via InfiniBand or RoCE v2 and must be isolated from the management plane. The management plane handles SSH, SLURM, and monitoring traffic. The storage plane provides NFS or GPFS access for datasets and checkpoints. Cross-plane access must pass through firewalls with allowlist-only rules.
Micro-segmentation using eBPF-based network policies prevents lateral movement. A compromised training container should only access its assigned GPUs, a specific NCCL multicast group, and authorized storage mounts. Cilium with Kubernetes network policies reduces the blast radius by 95 percent compared to flat network design.
COMPLIANCE FRAMEWORKS AND AUDIT
GPU clusters supporting regulated workloads must meet SOC 2 Type II, HIPAA, or FedRAMP Moderate. SOC 2 requires GPU access audit logs with 1-year retention, key management via HSM, and quarterly penetration testing. HIPAA requires BAA with cloud provider, GPU memory sanitization certification, and ePHI data encryption at rest and in transit.
Audit logging at GPU granularity captures: job submission time, GPU allocation time, duration, user identity, model/script executed, GPU memory regions accessed, data volume transferred to/from storage, and network connections. A 256-GPU cluster generates 8-12 GB of structured audit logs daily. Automated log analysis with anomaly detection identifies 90 percent of suspicious access patterns.