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From 3 TB RAM to 96 GB: superseding billion vector HNSW with 40x cheaper DiskANN

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In this 3-part series, I deep dive into vector search and ANN indexing, the limitations of scaling popular algorithms like HNSW, and how I built an open source vector DB for querying across billions of vectors on commodity machines, called CoreNN. For example, here it is searching across 1 billion Reddit comment embeddings in 15 ms from a 4.8 TB index on disk—all time is spent embedding the query and fetching results: In the previous post, I provide an introduction and explanation to the problem of searching across vectors (like neural embeddings) efficiently, and the intuition and theory behind why graph-based algorithms like HNSW solve the problem. In this post, I'll go into the limitations of HNSW that make it difficult to use in production systems at scale, and dive deep into DiskANN, an approach that leverages the high recall and flexibility of graphs while making them disk-native, making it possible to cost effectively serve and query billion-scale indices of embeddings. DiskANN

In this 3-part series, I deep dive into vector search and ANN indexing, the limitations of scaling popular algorithms like HNSW, and how I built an open source vector DB for querying across billions of vectors on commodity machines, called CoreNN . For example, here it is searching across 1 billion Reddit comment embeddings in 15 ms from a 4.8 TB index on disk—all time is spent embedding the query and fetching results: In the previous post , I provide an introduction and explanation to the problem of searching across vectors (like neural embeddings ) efficiently, and the intuition and theory b

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