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VQ-bench: A Composable Vector Quantization Framework

2026-07-31 15:24:12
Ashwin Padaki, Amir Ingber, Edo Liberty
arXiv_AI
       
Abstract

Vector quantization is an old problem but has recently become central to AI infrastructure. It is therefore experiencing a surge of renewed engineering and research activity. This paper provides a unified framework for developing and benchmarking new quantization algorithms. We describe 7 common conceptual quantization primitives and show how to compose them arbitrarily. We then re-express 25 common quantizers as pipelines of these primitives. Finally, we publish VQ-bench as open-source to be extended further and make reproducible benchmarks publicly available.

Abstract (translated)

URL

https://arxiv.org/abs/2608.11240

PDF

https://arxiv.org/pdf/2608.11240.pdf

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