IBM worked with Nvidia and Samsung to demonstrate a content-aware storage (CAS) system that can hold a 100-billion-vector database on a single server, work targeted at making retrieval-augmented ...

Enterprise data teams moving agentic AI into production are hitting a consistent failure point at the data tier. Agents built across a vector store, a relational database, a graph store and a ...

TL;DR: KIOXIA's AiSAQ technology, combined with NVIDIA's cuVS Library, enables efficient scaling of high-dimensional vector searches to 4.8 billion vectors on a single server, achieving up to 20X ...

Composite data for country groups in the WEO are either sums or weighted averages of data for individual countries. Arithmetically weighted averages are used for all data for the emerging market and ...

In this tutorial, we build an elastic vector database simulator that mirrors how modern RAG systems shard embeddings across distributed storage nodes. We implement consistent hashing with virtual ...

Microsoft has unveiled a new generation of Azure virtual machines (VMs) built on AMD’s 5th Gen EPYC processors, designed for cloud, AI, and high-performance computing. The newly introduced Da/Ea/Fasv7 ...

A new open-source framework called PageIndex solves one of the old problems of retrieval-augmented generation (RAG): handling very long documents. The classic RAG workflow (chunk documents, calculate ...

Endee.io launches Endee, an open source vector database delivering fast, accurate, and cost-efficient AI and semantic search at scale. Endee rethinks vector DBs for high recall, low latency, and low ...

This voice experience is generated by AI. Learn more. This voice experience is generated by AI. Learn more. Every enterprise IT executive faces the same AI paradox: their most valuable data sits ...

TL;DR: KIOXIA's open-source AiSAQ technology reduces DRAM needs by offloading vectorized AI data to SSDs, enabling scalable, low-latency Retrieval Augmented Generation (RAG) pipelines. Its integration ...