How the Baseten Delivery Network (BDN) makes cold starts fast

The Baseten Delivery Network (BDN) is an innovative weight delivery system designed to optimize the deployment of machine learning models by reducing runtime dependency on external providers and minimizing costs associated with weight transfers. By implementing a three-tier cache hierarchy—comprising node-local disk, an in-cluster peer cache, and a mirrored origin—BDN ensures fast cold starts and minimizes the impact of unreliable upstream weight transfers, such as those from providers like Hugging Face, S3, and GCS. This system mirrors model weights into secure storage during deployment, allowing for efficient management and retrieval. It coordinates downloads across clusters to manage bandwidth during scale events and uses metadata-based mirroring to prevent redundant data transfers, ensuring that identical files are stored only once. BDN's architecture improves data transfer speeds and reliability, achieving high throughput by leveraging parallelism and optimized chunk-transfer implementations. It also employs strategies like LRU eviction to manage cache resources effectively. The system, while initially focused on model weights, is being expanded to handle other deployment artifacts, contributing to the development of a robust infrastructure for AI workloads.

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