Scalar Quantization

Scalar Quantization, abbreviated SQ, is a vector compression technique that maps each float32 component of a vector to a lower-precision representation — typically int8 (8 bits) or even binary (1 bit) — by linearly scaling the value range to fit the smaller integer type. SQ achieves 4x compression (float32 to int8) or 32x compression (float32 to binary), reducing memory footprint and accelerating distance computations through SIMD integer arithmetic. Compared to Product Quantization, SQ is simpler to implement and tune but typically achieves less compression for similar accuracy loss. Modern vector databases like Milvus, Qdrant, and Weaviate offer SQ as a tunable compression option, often with rescoring against full-precision vectors for the top candidates to recover accuracy. Binary SQ has become particularly popular for embedding models that support it natively, such as Cohere Embed v3 and Mixedbread mxbai-embed, which produce embeddings designed to remain accurate after binarization. AI governance teams adopting SQ document the compression configuration as part of their embedding pipeline lineage for AI compliance traceability.

Scalar quantization at scale with Centralpoint: Centralpoint integrates quantization-aware vector backends so cost-sensitive workloads can compress aggressively while compliance-critical workloads stay at full precision. The model-agnostic platform meters tokens, keeps prompts local, and deploys SQ-backed chatbots across portals with one line of JavaScript.


Related Keywords:
Scalar Quantization,,