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Kioxia’s algorithm enables vector searches directly on SSDs and reduces host memory requirements letting system architects fine-tune the optimal balance among RAG systems for a variety of contrasting workloads without the need to store index data in DRAM

July 8, 2025 //  by Finnovate

Kioxia Corporation, a world leader in memory solutions, announced an update to its KIOXIA AiSAQ (All-in-Storage ANNS with Product Quantization) software. This new open-source release introduces flexible controls allowing system architects to define the balance point between search performance and the number of vectors, which are opposing factors in the fixed capacity of SSD storage in the system. The resulting benefit enables architects of RAG systems to fine tune the optimal balance of specific workloads and their requirements, without any hardware modifications.  KIOXIA AiSAQ software uses a novel approximate nearest neighbor search (ANNS) algorithm that is optimized for SSDs and eliminates the need to store index data in DRAM. By enabling vector searches directly on SSDs and reducing host memory requirements, KIOXIA AiSAQ technology allows vector databases to scale, largely without the restrictions caused by limited DRAM capacity.  This latest update allows administrators to select the optimal balance for a variety of contrasting workloads among the RAG system. This update makes KIOXIA AiSAQ technology a suitable SSD-based ANNS for not only RAG applications but also other vector-hungry applications such as offline semantic searches. With growing demand for scalable AI services, SSDs offer a practical alternative to DRAM for managing the high throughput and low latency that RAG systems require. KIOXIA AiSAQ software makes it possible to meet these demands efficiently, enabling large-scale generative AI without being constrained by limited memory resources.

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