Ingesting TB Scale Data Within An Hour For Scalable RAG
DDN, Wednesday, March 18th, 2026
DDN Infinia with NVIDIA cuVS accelerates vector indexing throughput at scale
As companies scale up their AI factory workloads, Retrieval-Augmented Generation (RAG) pipelines are scaling to tens and hundreds of millions of embeddings. There is a direct relationship between increasing enterprise knowledge available to AI agents and the size of vector embeddings. As scale increases, vector ingestion and indexing become the bottlenecks where users feel the pain of out-of-date knowledge bases and taking days to weeks to ingest data into their vector database. Enterprises endeavor to provide the best end user experience with fresh, accurate, and comprehensive responses, but CPU-only indexing pipelines and local-storage-centric architectures make it impractical to update data fast enough to keep knowledge bases current. Moreover, GPU-accelerated indexing enables faster database re-indexing because of pipeline updates or data drift, reducing maintenance downtime.