Clear models, fast starts: building Runpod's model store

The development of Model Store and a private Model Repository was driven by the need to mitigate the inefficiencies of repeatedly downloading large model weights for serverless GPU inference, which incurs high costs and latency. By implementing a tiered caching system—starting with host-local disks, then datacenter-scoped network volumes, and finally remote origins—this solution optimizes the distribution of model artifacts. Key innovations include explicit model version pinning for reliable rollouts, locality-aware placement to prioritize hosts with cached models, and a scheduling mechanism that ensures models are verified and available before worker deployment. The architecture involves three projects: runpod, which orchestrates scheduling and tracks model assignments; host, which manages local model states and transfers; and proxy, which facilitates communication without additional logic. This system addresses challenges of cold-start latency, network dependency, and redundant downloads, providing a robust framework for scaling model-serving endpoints. The methodology emphasizes understanding the location and management of model bytes, treating them as integral rather than incidental to deployment, and utilizing orchestration for reliable runtime behavior.

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