GPU Fractioning Explained: How to Run Multiple AI Workloads on a Single GPU

GPU fractioning, a method for dividing a single physical GPU into multiple logical units, is increasingly vital due to the high demand for GPUs driven by AI workloads. This approach maximizes hardware utilization, reduces operational costs, and allows diverse AI tasks to run concurrently on a single GPU. Techniques such as TimeSlicing and NVIDIA's Multi-Instance GPU (MIG) enable this process by allowing multiple workloads to share GPU resources, either through software-level divisions or hardware-based isolation. TimeSlicing allocates time-based slices of GPU resources, offering flexibility but with potential interference risks, while MIG provides strong isolation with fixed configurations but limited dynamic sharing. Clarifai's Compute Orchestration simplifies GPU fractioning by managing the complexity of resource allocation and workload scaling, ensuring efficient utilization without manual setup. This orchestration layer intelligently adjusts resources in real-time, allowing developers to focus on applications rather than infrastructure, and enabling seamless scaling from prototype to production.

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