SPHQuant: Efficient extreme low bit weight quantization for Vision-Language Models

Paper recorded by Signals 4 on 2026-09-21 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-21 on arXiv · recorded by Signals 4 on 2026-09-22

Category: cs.CV · 计算机视觉 · first seen 2026-09-22

Abstract

Recent foundation models are moving toward native multimodal Vision-Language Models (VLMs), making VLMs a central form of next-generation foundation models. However, their large language backbones make edge deployment difficult due to high memory footprint and memory-bound autoregressive decoding. Weight-only post-training quantization is a practical solution, but pushing VLMs to extreme low bit-w

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