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cuVS is an open-source library from RAPIDS for fast, GPU-accelerated vector search, useful for building high-performance vector databases.
RAFT is a suite of GPU-accelerated libraries for data science, including support for vector search and similarity operations, often used in vector database scenarios.
Milvus is a mature, open-source vector database maintained by Zilliz, supporting large-scale similarity search with multiple indexing strategies and GPU acceleration. It includes variants such as Milvus Lite (lightweight version), Milvus Standalone (single-machine deployment), and Milvus Distributed (Kubernetes-based deployment for large scale).
NVIDIA CAGRA is a GPU-accelerated graph-based library for approximate nearest neighbor searches, optimized for high-performance vector search leveraging modern GPU parallelism. It is suitable for scenarios requiring rapid, large-scale vector retrieval.
BANG is a billion-scale approximate nearest neighbor search system optimized for single GPU execution, enabling high-performance vector search in vector database environments at massive scale.
Arroy is an open-source library for efficient similarity search and management of vector embeddings, useful in vector database systems.
Source: GitHub - rapidsai/cuvs
Category: Open-source
Tags: open-source, gpu-acceleration, vector-search, high-performance
cuVS is an open-source library from RAPIDS for fast, GPU-accelerated vector search and clustering, designed to simplify and accelerate vector similarity search and clustering tasks using state-of-the-art algorithms on NVIDIA GPUs. It is derived from the RAPIDS RAFT library and will become its primary home for approximate nearest neighbors (ANN) and clustering algorithms in late 2024.
cuVS is open-source and free to use under its license.
cuVS is released under an open-source license (see the repository for details).