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arXiv · 2509.18471

Individualized non-uniform quantization for vector search

Mariano Tepper·Ted Willke
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Abstract

Embedding vectors are widely used for representing unstructured data and searching through it for semantically similar items. However, the large size of these vectors, due to their high-dimensionality, creates problems for modern vector search techniques: retrieving large vectors from memory/storage is expensive and their footprint is costly. In this work, we present NVQ (non-uniform vector quantization), a new vector compression technique that is computationally and spatially efficient in the high-fidelity regime. The core in NVQ is to use novel parsimonious and computationally efficient nonlinearities for building non-uniform vector quantizers. Critically, these quantizers are \emph{individually} learned for each indexed vector. Our experimental results show that NVQ exhibits improved accuracy compared to the state of the art with a minimal computational cost.

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Mariano Tepper, Ted Willke. 2025-09-22. Individualized non-uniform quantization for vector search. https://arxiv.org/abs/2509.18471

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