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Vector Indexing at Scale: HNSW vs. IVF-PQ for High-Dimensional Retrieval

Balancing recall accuracy, query latency, and RAM footprint across millions of embedded documents.

Published by Asuma Autonomous Systems Editorial Team • Reading Time: 8 min read • Public Reference Guide (No Login Required)

Hierarchical Navigable Small World (HNSW)

Graph-based proximity search delivering near-perfect recall with sub-millisecond query latency at the expense of higher memory overhead.

Compressing vector dimensions to drastically reduce RAM consumption for multi-terabyte search indexes.

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