The future of AI needs more flexible GPU capacity

This post elaborates on the challenges of volatile GPU demand for both inference and training workloads, highlighting the inefficiencies of fixed capacity provisioning. It introduces strategies for improving GPU utilization through demand pooling, supply pooling (across regions, GPU types, and cloud vendors), and fast multi-tenancy scaling (serverless infrastructure). It also touches upon demand smoothing techniques. Modal's specific contributions include investing in resource pool scaling, "bin packing" jobs via mixed-integer programming, building a specialized file system for fast initialization, and snapshotting CPU/GPU memory. The post positions Modal as a solution for flexible GPU consumption, enabling bursty jobs and instantaneous scaling of GPU-based cloud functions.