Scaling RAG with NVME: DISKANN's Hybrid Approach to Vector Databases Indexing
Is it still realistic to rely solely on DRAM for vector index storage when Large Language Models are driving petabyte-scale growth? Traditional in-memory indexing strategies quickly exhaust host memory as vector collections expand. DISKANN (Disk-Accelerated Approximate Nearest Neighbor) is a hybrid vector search algorithm developed by Microsoft, designed to offload portions of the search index to NVMe SSDs.