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cuVS - a library for vector search and clustering on the GPU
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| 2026-07-24 | 822 |
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# <div align="left"><img src="https://rapids.ai/assets/images/rapids_logo.png" width="90px"/> cuVS: Vector Search and Clustering on the GPU</div> ## Contents 1. [Useful Resources](#useful-resources) 2. [What is cuVS?](#what-is-cuvs) 3. [Installing cuVS](#installing-cuvs) 4. [Getting Started](#getting-started) 5. [Contributing](#contributing) 6. [References](#references) ## Useful Resources - [Documentation](https://docs.nvidia.com/cuvs): Library documentation. - [Build and Install Guide](https://docs.nvidia.com/cuvs/installation): Instructions for installing and building cuVS. - [Getting Started Guide](https://docs.nvidia.com/cuvs/getting-started): Guide to getting started with cuVS. - [Code Examples](https://github.com/nvidia/cuvs/tree/HEAD/examples): Self-contained Code Examples. - [API Reference Documentation](https://docs.nvidia.com/cuvs/api_reference): API Documentation. - [RAPIDS Community](https://rapids.ai/community.html): Get help, contribute, and collaborate. - [GitHub repository](https://github.com/nvidia/cuvs): Download the cuVS source code. - [Issue tracker](https://github.com/nvidia/cuvs/issues): Report issues or request features. ## What is cuVS? cuVS contains state-of-the-art implementations of several algorithms for running approximate nearest neighbors and clustering on the GPU. It can be used directly or through the various databases and other libraries that have integrated it. The primary goal of cuVS is to simplify the use of GPUs for vector similarity search and clustering. Vector search is an information retrieval method that has been growing in popularity over the past few years, partly because of the rising importance of multimedia embeddings created from unstructured data and the need to perform semantic search on the embeddings to find items which are semantically similar to each other. Vector search is also used in _data mining and machine learning_ tasks and comprises an important step in many _clustering_ and _visualization_ algorithms like [UMAP](https://arxiv.org/abs/2008.00325), [t-SNE](https://lvdmaaten.github.io/tsne/), K-means, and [HDBSCAN](https://hdbscan.readthedocs.io/en/latest/how_hdbscan_works.html). Finally, faster vector search enables interactions between dense vectors and graphs. Converting a pile of dense vectors into nearest neighbors graphs unlocks the entire world of graph analysis algorithms, such as those found in [GraphBLAS](https://graphblas.org/) and [cuGraph](https://github.com/rapidsai/cugraph). Below are some common use-cases for vector search - ### Semantic search - Generative AI & Retrieval augmented generation (RAG) - Recommender systems - Computer vision - Image search - Text search - Audio search - Molecular search - Model training - ### Data mining - Clustering algorithms - Visualization algorithms - Sampling algorithms - Class balancing - Ensemble methods - k-NN graph construction ## Why cuVS? There are several benefits to using cuVS and GPUs for vector search, including 1. Fast index build 2. Latency critical and high throughput search 3. Parameter tuning 4. Cost savings 5. Interoperability (build on GPU, deploy on CPU) 6. Multiple language support 7. Building blocks for composing new or accelerating existing algorithms In addition to the items above, cuVS shoulders the burden of keeping non-trivial accelerated code up to date as new NVIDIA architectures and CUDA versions are released. This provides a delightful development experience, guaranteeing that any libraries, databases, or applications built on top of it will always be getting the best performance and scale. ## cuVS Technology Stack cuVS is built on top of the RAPIDS RAFT library of high performance machine learning primitives and provides all the necessary routines for vector search and clustering on the GPU. ![cuVS is built on top of low-level CUDA libraries and provides many important routines that enable vector search and clustering on the GPU]
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gpuCI
221
Ben Frederickson · Canada
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Bradley Dice · @NVIDIA @rapidsai
77
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James Lamb · NVIDIA · Israel
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Corey J. Nolet · @Nvidia @rapidsai @cuml @cuvs · United States
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Jake Awe
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Divye Gala · NVIDIA · United States
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Victor Lafargue · NVIDIA · Switzerland
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Ray Douglass
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:07e21369975cda8d, topic:vector-search, topic:vector-store, topic:similarity-search
matched fp:07e21369975cda8d, topic:gpu, topic:cuda