AI Storage Blueprint#

Sidharth Bajaj (Meta), Venkatraghavan Srinivasan (Meta)

• Why: In training clusters, storage is frequently what gates GPU utilization (checkpoint I/O, dataset streaming, data-loading jitter). Meta re-architecting its storage stack around AI workloads is the storage-side counterpart of the GB200 scheduling talk.
• What: How Meta evolved its storage architecture to serve an environment where frontier models ship in weeks, framed around two explicit optimization targets: GPU utilization and research velocity. (from abstract only)
• How: Not detailed in the abstract; the two stated hurdles imply work on throughput/latency consistency for training data paths and on flexibility/speed of the research workflow.
• Where applicable: Meta-specific production setup; the framing (storage judged by GPU-hours protected and researcher iteration speed) is a useful lens for any AI-cluster storage design even before the details land.