Vector Search

In the era of generative AI, vector databases have become indispensable for storing and querying high-dimensional data efficiently. However, like all databases,...

In the first part of the series, we presented an overview of the IVF-PQ algorithm and explained how it builds on top of the IVF-Flat algorithm, using the...

In this post, we continue the series on accelerating vector search using NVIDIA cuVS. Our previous post in the series introduced IVF-Flat, a fast algorithm for...

Performing an exhaustive exact k-nearest neighbor (kNN) search, also known as brute-force search, is expensive, and it doesn’t scale particularly well to...

In this post, we dive deeper into each of the GPU-accelerated indexes mentioned in part 1 and give a brief explanation of how the algorithms work, along with a...

In the current AI landscape, vector search is one of the hottest topics due to its applications in large language models (LLM) and generative AI. Semantic...

RAPIDS is a suite of accelerated libraries for data science and machine learning on GPUs: cuDF for pandas-like data structures cuGraph for graph data cuML for...

Relying on the capabilities of GPUs, a team from Facebook AI Research has developed a faster, more efficient way for AI to run similarity searches. The study,...