Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
MxMoE: Mixed-precision Quantization for MoE with Accuracy and Performance Co-Design
Authors: Haojie Duanmu, Xiuhong Li, Zhihang Yuan, Size Zheng, Jiangfei Duan, Xingcheng Zhang, Dahua Lin
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Evaluations show that Mx Mo E outperforms existing methods, achieving 2.4 lower Wikitext-2 perplexity than GPTQ at 2.25-bit and delivering up to 3.4 speedup over full precision, as well as up to 29.4% speedup over uniform quantization at equivalent accuracy with 5-bit weight-activation quantization. |
| Researcher Affiliation | Collaboration | 1Shanghai Jiao Tong University 2Shanghai AI Laboratory 3Peking University 4Byte Dance Seed 5The Chinese University of Hong Kong 6CPII under Inno HK. |
| Pseudocode | No | The paper describes the methodology in prose and mathematical formulas, but does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/cat538/Mx Mo E. |
| Open Datasets | Yes | For all experiments, we use 128 sequences, each of length 4096, drawn from the Wikitext2 training set (Merity et al., 2016). This calibration process typically takes from several minutes to a few hours depending on the model size. |
| Dataset Splits | No | The paper mentions using 128 sequences from the Wikitext2 training set for calibration and randomly sampling sequences from Wiki Text-2 for performance analysis, but it does not provide specific training/validation/test splits for the models or benchmark tasks evaluated. |
| Hardware Specification | Yes | Experiments conducted on Nvidia RTX-4090. |
| Software Dependencies | No | The paper mentions several software components like CUTLASS, HQQ, VLLM-Marlin-MoE, Marlin, GPTQ, and CUDA, but does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | For weight-only quantization we test 3-bit and 2-bit quantization, comparing with GPTQ, configured with group size 128, asymmetric min-max quantization, where the scale and zero-point are stored in 16-bit format, resulting in an average bitwidth of 3.25 and 2.25, respectively. ... Mx Mo E use r = 1 as extremely low-bitwidth implies resource-constrained environment... Mx Mo E use r = 0.75. |