Kioxia Corporation, a world leader in memory solutions, announced the open-source release of its new All-in-Storage ANNS with Product Quantization (AiSAQ) technology. A novel “approximate nearest neighbor” search (ANNS) algorithm optimized for SSDs, KIOXIA AiSAQ™ software delivers scalable performance for retrieval-augmented generation (RAG) without placing index data in DRAM – and instead searching directly on SSDs.
Generative AI systems demand significant compute, memory and storage resources. While they have the potential to drive transformative breakthroughs across various industries, their deployment often comes with high costs. RAG is a critical phase of AI that refines large language models (LLMs) with data specific to the company or application.
A central component of RAG is a vector database that accumulates and converts specific data into feature vectors in the database. RAG also utilizes an ANNS algorithm, which identifies vectors that improve the model based on similarity between the accumulated and target vectors. For RAG to be effective, it must rapidly retrieve the information most relevant to a query. Traditionally, ANNS algorithms are deployed in DRAM to achieve the high-speed performance required for these searches.
KIOXIA AiSAQ technology provides a scalable and efficient ANNS solution for billion-scale datasets with negligible memory usage and fast index switching capabilities.
Key Benefits of KIOXIA AiSAQ Technology:
- Allows large-scale databases to operate without relying on limited DRAM resources, enhancing the performance of RAG systems.
- Eliminates the need to load index data into DRAM, enabling the vector database to launch instantly. This supports seamless switching between user-specific or application-specific databases on the same server for efficient RAG service delivery.
- Optimized for cloud systems by storing indexes in disaggregated storage for sharing across multiple servers. This approach dynamically adjusts vector database search performance for specific users or applications and facilitates the rapid migration of search instances between physical servers.
ソース ビジネスワイヤー
