Binary and Scalar Embedding Quantization in Sentence Transformers and Qdrant Vector Stores

AI Impact Summary

Embedding quantization introduces a post-processing step to convert float32 embeddings into binary or int8 representations, enabling dramatically reduced memory and storage footprints and up to 32x faster retrieval. By using binary embeddings for initial candidate retrieval via Hamming distance and a rescoring pass with full-precision embeddings, production pipelines can preserve 92-96% of end-to-end accuracy while slashing latency and cost. The approach spans popular models (e.g., all-MiniLM-L6-v2, all-mpnet-base-v2, OpenAI text-embedding-3-small/large) and works with vector stores such as Qdrant, offering a clear path to cheaper, scalable vector search deployments.