New Framework Enables Runtime Switchable Quantization for MoE Models

Global: DynaMo: Runtime Switchable Quantization for MoE with Cross-Dataset Adaptation

Researchers led by Zihao Zheng and colleagues released a new quantization framework called DynaMo that allows mixture‑of‑experts (MoE) neural networks to switch precision settings at runtime, addressing performance gaps when models are applied to different datasets. The work was first submitted to arXiv on March 27, 2025 and revised on January 9, 2026.

Background

MoE architectures increase model capacity by routing inputs to specialized expert sub‑networks, but the added parameters raise memory and compute demands. Existing quantization techniques typically apply a static precision across the entire model, which can hinder adaptability when the model encounters data distributions that differ from the training set.

Methodology

According to the authors, DynaMo begins with a multi‑level analysis that quantifies the importance of each expert and each channel within the MoE. The framework then employs an expert‑level mixed‑precision baseline, ensuring that the quantized model remains compatible with a variety of established datasets. A channel‑level dynamic switching mechanism is added to enable the model to adjust its precision on‑the‑fly when presented with novel data.

Experimental Results

The authors report that DynaMo achieves a perplexity reduction ranging from 2.78 to 4.54 points and an accuracy gain between 1.85% and 3.77% across several benchmark datasets. In addition, the system delivers roughly a three‑fold inference speedup while incurring only negligible computational overhead.

Implications for Deployment

By combining mixed‑precision baselines with runtime adaptability, DynaMo could lower the hardware requirements for large‑scale MoE models, making them more viable for edge devices and real‑time applications without sacrificing accuracy.

Future Directions

The paper suggests further investigation into automated expert importance scoring and broader testing on heterogeneous hardware platforms to validate the framework’s generality.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.