DalQ: Reconciling Accuracy and Efficiency in Vector Quantization

Vector quantization is a fundamental technique for reducing memory footprint and computational overhead in modern database systems, search engines, and large language models. However, existing vector quantization methods remain limited in simultaneously achieving high quantization accuracy and computational efficiency. In this paper, we propose Distribution-aware locally-adaptive Quantization (DalQ), which achieves superior accuracy with competitive efficiency through two key mechanisms. First, distribution-aware range adaptation leverages Gaussian-like embedding distributions via an adaptive clip factor, concentrating precision in high-density regions while tolerating controlled clipping of rare outliers. Second, decomposed vector refinement employs a two-stage geometric optimization strategy for near-optimal reconstruction: directional alignment via iterative code search and magnitude refinement via projection-based scaling. We prove the existence and uniqueness of the optimal clip factor and identify a reference clip factor enabling tuning-free deployment. Extensive evaluations demonstrate the substantial advantages of DalQ over state-of-the-art baselines. Against LVQ, DalQ achieves up to 96.6 × faster quantization and up to 3.2 × faster distance computation.