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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.

Cagra provides highly parallel graph construction and approximate nearest neighbor search for GPUs, supporting large-scale vector database operations and efficient similarity search.

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.

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.

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).