SOURCE-LINKED INTELLIGENCE

Activation Outliers Matter: Robust Recovery for Quantized Multimodal LLMs

arXiv · AI, language, vision and robotics · article · Aug 27, 2026 · UTC

Low-bit quantization offers a promising avenue for reducing the computational and memory demands of Multimodal Large Language Models (MLLMs). Recent hardware support for low-precision formats, ranging from MXFP8 to ultra-low-bit formats such as MXFP4 and HiF4, has accelerated research into efficient MLLM training and deployment. In this work, we present a systematic study of these quantization schemes in representative MLLMs that span both video generation and reasoning tasks. Our analysis shows that MXFP8 achieves near-lossless performance, whereas aggressive 4-bit quantization leads to signi

Read original source ↗ Open in workspace

recordType

paper

region

Global