Introduction

Start here to learn the main concepts behind cuVS, how they fit together, 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.