Getting Started

Use these pages to learn what NVIDIA cuVS does, where it fits in a vector-search system, and which guide to read next.

Introduction

Vector Search: learn the main index families, how they trade recall for performance, and where to start when choosing an index.
Clustering: compare clustering methods, including why K-Means is often the preferred partitioning method for large vector search systems.
Vector Compression: compare quantization and dimensionality reduction methods for reducing vector storage, bandwidth, and search cost.
Vector Database: understand the difference between a vector index and a vector database, including local, global, and hybrid partitioning.
Tuning Indexes: tune standalone indexes and vector databases for recall, latency, throughput, build time, and memory.

Integrations: see databases, libraries, and data platforms that expose NVIDIA cuVS-backed workflows.
Use-cases: review common vector-search, vector-database, clustering, preprocessing, and exploratory-analysis use-cases.

Use these pages to learn what NVIDIA cuVS does, where it fits in a vector-search system, and which guide to read next.

Vector Search: learn the main index families, how they trade recall for performance, and where to start when choosing an index.
Clustering: compare clustering methods, including why K-Means is often the preferred partitioning method for large vector search systems.
Vector Compression: compare quantization and dimensionality reduction methods for reducing vector storage, bandwidth, and search cost.
Vector Database: understand the difference between a vector index and a vector database, including local, global, and hybrid partitioning.
Tuning Indexes: tune standalone indexes and vector databases for recall, latency, throughput, build time, and memory.

Integrations: see databases, libraries, and data platforms that expose NVIDIA cuVS-backed workflows.
Use-cases: review common vector-search, vector-database, clustering, preprocessing, and exploratory-analysis use-cases.