Hugging Face· Platform

Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval

Hugging Face introduces binary and scalar embedding quantization techniques to improve retrieval speed, reduce memory usage, and lower costs. The methods involve converting float32 embeddings to 1-bit (binary) or int8 (scalar) values, enabling 32x reduction in memory. A demo with 41 million Wikipedia texts demonstrates practical benefits. Experiments show up to ~96% performance retention with rescoring.

From the source

We introduce the concept of embedding quantization and showcase their impact on retrieval speed, memory usage, disk space, and cost.

huggingface.co

Announced

22 Mar 2024 00:00 UTC

Detected

03 Aug 2026 21:12 UTC (+864d after)