KIOXIA Achieves 4.8 Billion High-Dimensional Vector Search Database on a Single Server, with 7.8x Index Build Time Acceleration via GPUs

TOKYO, March 17 (Bernama) -- Kioxia Corporation today announced the successful demonstration of achieving high-dimensional vector search scaling to 4.8 billion vectors on a single server with its open-source KIOXIA AiSAQ™ approximate nearest neighbor search (ANNS) technology. Additionally, Kioxia demonstrated a significant reduction in index build time by leveraging GPU acceleration through NVIDIA cuVS. These two achievements mark a significant advancement for retrieval augmented generation (RAG) search solutions. Continued development is underway to support larger-scale deployments beyond 4.8 billion vectors.

Index build time on a massive-scale vector database is a crucial pain point for the industry. In collaboration with NVIDIA, Kioxia demonstrated up to 20x improvement in KIOXIA AiSAQ index build time for high-dimensional vectors of 1024 dimensions, and up to 7.8x improvement in end-to-end build times. This 20x improvement represents a reduction from 28.4 days using CPU to 1.4 days using four NVIDIA Hopper GPUs to build the index, and a reduction from 31 days to 4 days in end-to-end testing.1