GPU Isolation and Multi-Tenancy
GPU multi-tenancy maximizes hardware ROI by allowing multiple independent workloads to share a single GPU securely and efficiently.
Source: mortalapps.com- GPU multi-tenancy maximizes hardware ROI by allowing multiple independent workloads to share a single GPU securely and efficiently.
- Its core purpose is to prevent severe resource underutilization caused by assigning dedicated, massive GPUs to low-demand or highly sporadic tasks.
- The primary optimization idea relies on choosing the correct isolation boundary: Multi-Instance GPU (MIG) for strict hardware isolation, or Multi-Process Service (MPS) for high-throughput cooperative sharing.
- The most important engineering insight is that MIG partitions the memory and fault domains physically, whereas MPS time-slices execution and shares memory space, sacrificing strict isolation for much higher concurrency.
Why This Matters
A single NVIDIA H100 GPU costs upwards of $30,000. If an infrastructure engineer deploys a lightweight 8B parameter model to a dedicated H100, up to eighty percent of the VRAM and compute capacity sits entirely idle. Effective multi-tenancy allows cluster administrators to mathematically pack multiple models, data science notebooks, or inference services onto that single piece of hardware, fundamentally changing the unit economics of the datacenter from wasteful to highly efficient.
Core Intuition
Think of a GPU as an expensive commercial office building.
Time-Slicing: Everyone shares the whole building, taking turns using the rooms. It's chaotic, unstructured, and a single bad actor can easily crash the whole building.
MPS (Multi-Process Service): An intelligent manager dynamically coordinates everyone, allowing them to use different rooms concurrently. It's highly efficient, but because they share hallways, one malicious actor can still trigger a fire alarm that affects everyone.
MIG (Multi-Instance GPU): The building is physically walled off into separate, soundproof suites. It's rigid and unchangeable, but what happens in one suite mathematically cannot affect the others.
Technical Deep Dive
MIG provides true hardware isolation. Supported on Ampere (A100) and Hopper (H100) architectures, it divides the GPU into up to seven fully isolated sub-GPUs (using profiles like 1g.5gb). These instances share only the PCI interface bandwidth towards the CPU. Crucially, memory, cache, and compute cores are physically partitioned at the hardware level, ensuring strict Quality of Service (QoS) and absolute fault isolation. Conversely, MPS is a software concurrency solution based on a client-server runtime service. The MPS control daemon actively routes multiple CUDA contexts onto a single GPU concurrently. Because these contexts share the exact same physical memory space, context-switching overhead is virtually eliminated, and SM utilization skyrockets for small, cooperative workloads. However, a severe segmentation fault in one process can crash the MPS daemon, bringing down all tenants simultaneously.