Which vector index is known for its efficiency in memory for vector similarity searches?

The choice of HNSW (Hierarchical Navigable Small World graphs) as the answer stems from its design, which allows for efficient memory usage while conducting vector similarity searches. HNSW utilizes a layered graph structure that aims to connect data points in such a way that allows for both fast and memory-efficient statistics.

The key advantage of the HNSW algorithm is its ability to maintain a good balance between search quality and resource consumption, which makes it highly efficient in terms of memory relative to other indexing methods. It effectively reduces the complexity of searching through vast datasets, while still providing high recall rates and minimizing the number of candidates considered.

When analyzing other vector index methods in the context of memory efficiency, IVF (Inverted File) can be effective, but it might sacrifice some speed and accuracy compared to HNSW. VLAD (Vector of Locally Aggregated Descriptors) is better suited for specific types of visual recognition tasks and can require significant memory overhead based on how features are stored. KDT (Kd-tree) might not scale well to high-dimensional spaces where data sparsity can degrade search performance.

Thus, the properties of HNSW make it particularly well-suited for applications requiring both efficiency in memory usage and performance in vector similarity searches