researchMar 22
Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval
Researchers at Hugging Face propose binary and scalar embedding quantization methods to accelerate and reduce the cost of vector search. These methods can lead to significant speedups and cost reductions for dense retrieval systems. You can integrate these quantization methods into your existing retrieval pipelines. The proposed methods are compatible with various indexing and search algorithms.
Key takeaways
- Binary and scalar quantization reduce embedding dimensionality.
- Up to 4x faster and 8x cheaper vector search.
- Compatible with existing indexing and search algorithms.