Primary source reports sembed-engine Vamana implementation reduced w2v p50 query latency from 25.15ms to 1.524ms and gvec from 4.094ms to 0.631ms by replacing shared_ptr<Vector> objects with flat arrays and lightweight views while preserving exact node visit count of 64.625 and recall of 1.0 (https://dubeykartikay.com/posts/sembed-engine-vector-search-performance/). Build time on w2v fell from 17.91s to 1.889s.

DiskANN paper details analogous in-memory graph techniques that prioritize cache-efficient layouts and reduced floating-point operations to enable billion-scale nearest neighbor search on single nodes (https://arxiv.org/abs/1906.03640). FAISS library similarly eliminates square roots via squared Euclidean distances and employs blocked memory layouts for SIMD efficiency in production vector search (https://github.com/facebookresearch/faiss).

Original coverage omitted explicit linkage between these hot-path changes and compounding effects on AI serving costs where vector search dominates RAG retrieval latency; primary metrics show each distance computation now incurs fewer cache misses, indirections and recomputations at scale.