Solving the MoE Memory Puzzle: PuzzleMoE Delivers 50% Compression & Faster Inference for Large Language Models
By Yushu Zhao, Zheng Wang, Minjia Zhang
Published on November 10, 2025| Vol. 1, Issue No. 1
Content Source
This is a curated briefing. The original article was published on cs.LG updates on arXiv.org.
Summary\
Mixture-of-Experts (MoE) models offer efficient scaling for language models but face significant deployment hurdles due to high memory overhead from storing numerous expert parameters. PuzzleMoE introduces a novel, training-free compression method that resolves this challenge by performing sparse expert merging, identifying both shared and specialized parameters via a dual-mask. Furthermore, it incorporates a bit-packed encoding scheme, reusing underutilized exponent bits to efficiently store binary masks and signs. This dual approach enables PuzzleMoE to compress MoE models by up to 50% while preserving accuracy across various tasks, significantly outperforming prior compression methods and achieving up to 1.28x inference speedup.
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Why It Matters\
MoE models are pivotal for scaling large language models (LLMs) efficiently, offering superior performance with lower computational cost per query than their dense counterparts. However, their widespread adoption has been hampered by the formidable memory footprint required for vast numbers of specialized experts. PuzzleMoE directly addresses this critical bottleneck with a robust, training-free compression solution that crucially avoids the common trade-off between accuracy and efficiency. This innovation holds profound implications for the AI industry. Firstly, it significantly broadens the accessibility of powerful MoE architectures, making them viable for deployment on a wider spectrum of hardware, including resource-constrained environments like edge devices or systems with limited GPU memory. Secondly, by drastically cutting memory demands and boosting inference speed, PuzzleMoE substantially lowers the operational expenditures associated with deploying and running state-of-the-art LLMs, thereby accelerating their integration into commercial and industrial applications. This drive towards more efficient model architectures is a defining trend in AI, as the escalating complexity of models increasingly collides with practical limitations of computational resources and rising energy consumption. Ultimately, PuzzleMoE's success in "doing more with less" represents a vital advancement towards sustainable and scalable AI, ensuring that the transformative benefits of large, expert-driven models can be widely realized without mandating exascale infrastructure.