Introducing KIOXIA AiSAQ Technology for AI
Managing vector databases for AI and Retrieval Augmented Generation (RAG) workloads often requires prohibitively large DRAM footprints, making scalability a major challenge. This solution brief introduces KIOXIA AiSAQ™ technology, which moves vector indexes from DRAM to SSDs for an architecture that is both cost-effective and scalable. Download the solution brief for insights into overcoming DRAM limitations, and then contact ITontology to explore how this technology can work for your business.
KIOXIA AiSAQ (All-in-Storage ANNS with Product Quantization) is a vector search engine technology designed to enhance the scalability of vector databases while minimizing the reliance on DRAM. It allows organizations to perform efficient vector searches in Retrieval Augmented Generation (RAG) pipelines by leveraging SSD storage, thus addressing the growing concerns related to DRAM usage in large datasets.
How does AiSAQ improve DRAM utilization?
AiSAQ technology has demonstrated a significant reduction in DRAM utilization, achieving approximately a 396x decrease compared to traditional DRAM-based strategies. This is accomplished by offloading vector indexes to SSDs, allowing for the handling of larger datasets without the constraints of limited DRAM resources.
What role does Retrieval Augmented Generation (RAG) play?
RAG enhances LLMs by providing access to external information that the model may not have learned during its training. This technique allows LLMs to generate more accurate and relevant responses by retrieving context-specific data, thus avoiding the need for costly retraining while also enabling access to sensitive information without compromising the model's integrity.
Introducing KIOXIA AiSAQ Technology for AI
published by ITontology
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