VQ-bench: A Composable Vector Quantization Framework
Ashwin Padaki, Amir Ingber, Edo Liberty
cs.AI cs.DB
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.
Topics
Classified with taxonomy v2 on Sat, 5 Sept 2026.