# Binary Quantization - Andrey Vasnetsov | Vector Space Talks

DevFeed: [Binary Quantization - Andrey Vasnetsov | Vector Space Talks](<https://devfeed.tech/articles/binary-quantization-andrey-vasnetsov-vector-space-talks-46585.md>)

Original publisher: [Read original article](<https://qdrant.tech/blog/binary-quantization/>)

Author: info@qdrant.tech (Andrey Vasnetsov)

Published: 2024-01-09T10:30:10Z

Content type: article

Language: en

Sources: [Qdrant Blog on Qdrant - Vector Search Engine](<https://devfeed.tech/sources/qdrant-blog-on-qdrant-vector-search-engine.md>)

Topics: [quantization](<https://devfeed.tech/topics/quantization.md>), [AI search](<https://devfeed.tech/topics/ai-search.md>), [Qdrant](<https://devfeed.tech/topics/qdrant.md>), [AI, ML & Data Engineering](<https://devfeed.tech/topics/ai-ml-data-engineering.md>)

Tags: [approximate-nearest-neighbor-search](<https://devfeed.tech/tags/approximate-nearest-neighbor-search.md>), [bert](<https://devfeed.tech/tags/bert.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [fasttext](<https://devfeed.tech/tags/fasttext.md>), [hnsw](<https://devfeed.tech/tags/hnsw.md>), [image-search](<https://devfeed.tech/tags/image-search.md>), [knn-algorithm](<https://devfeed.tech/tags/knn-algorithm.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [matching](<https://devfeed.tech/tags/matching.md>), [neural-network](<https://devfeed.tech/tags/neural-network.md>), [qdrant](<https://devfeed.tech/tags/qdrant.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [recommender-system](<https://devfeed.tech/tags/recommender-system.md>), [saas](<https://devfeed.tech/tags/saas.md>), [semantic-search](<https://devfeed.tech/tags/semantic-search.md>), [simaes-networks](<https://devfeed.tech/tags/simaes-networks.md>), [similarity](<https://devfeed.tech/tags/similarity.md>), [similarity-search](<https://devfeed.tech/tags/similarity-search.md>), [speed](<https://devfeed.tech/tags/speed.md>), [storage](<https://devfeed.tech/tags/storage.md>), [talks](<https://devfeed.tech/tags/talks.md>), [transformer](<https://devfeed.tech/tags/transformer.md>), [vector](<https://devfeed.tech/tags/vector.md>), [vector-search](<https://devfeed.tech/tags/vector-search.md>), [vector-search-engine](<https://devfeed.tech/tags/vector-search-engine.md>), [vectors](<https://devfeed.tech/tags/vectors.md>), [word2vec](<https://devfeed.tech/tags/word2vec.md>)

## AI overview

A Vector Space Talks episode explains binary quantization for vector indexes and proximity-graph search. It covers reduced storage, faster processing, oversampling for real-time precision control, model compatibility, and Qdrant's architecture.

## Source excerpt

"Everything changed when we actually tried binary quantization with OpenAI model." - Andrey Vasnetsov Ever wonder why we need quantization for vector indexes? Andrey Vasnetsov explains the complexities and challenges of searching through proximity graphs. Binary quantization reduces storage size and boosts speed by 30x, but not all models are compatible. Andrey worked as a Machine Learning Engineer most of his career. He prefers practical over theoretical, working demo over arXiv paper. He is currently working as the CTO at Qdrant a Vector Similarity Search Engine, which can be used for semantic search, similarity matching of text, images or even videos, and also recommendations.