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.

jvector is a high-performance Java-based library and engine for vector search and approximate nearest neighbor indexing.

PilotANN is a memory-bounded GPU-accelerated framework for large-scale vector search, designed to improve performance and efficiency of approximate nearest neighbor (ANN) search workloads, making it relevant as a high-performance engine/component in vector database and vector search systems.

DiskANN is a graph-based approximate nearest neighbor search (ANNS) system optimized for fast and accurate billion-point nearest neighbor search on a single node, leveraging SSD storage. It is highly relevant for large-scale vector database applications requiring efficient vector search at scale.

cuVS is an open-source library from RAPIDS for fast, GPU-accelerated vector search, useful for building high-performance vector databases.

EFANNA is an extremely fast approximate nearest neighbor search algorithm based on kNN graphs and randomized KD-trees. The provided implementation offers a high-performance ANN index suitable as a building block in custom vector search and retrieval infrastructure.