Meta’s AI Storage Blueprint at Scale

This post details the evolution of Meta's BLOB-storage architecture to specifically address the demands of AI workloads. It highlights the challenges of storage bottlenecks impacting GPU utilization and research velocity, and introduces a rebuilt foundation with a unified metadata schema, a fat client SDK eliminating dataplane proxy, and regional deployments. It also covers strategies for handling spikes and hot spots with distributed data caches and readplan metadata caches, and protocol optimizations like hedged reads and dynamic concurrency control.