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        <title>Cubicpower Agentic Mixture of Experts(AMoE) Framework for Fine-Tuning NLP Tasks Without GPUs</title>
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        <namePart type="given">Chao-Yih</namePart>
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            <namePart type="given">Ke-Han</namePart>
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        <name type="personal">
            <namePart type="given">Chih-Kai</namePart>
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    <abstract>The rise of Green AI emphasizes minimizing the environmental footprint of AI systems. This paper explores a no-GPU agentic architecture for fine-tuning NLP tasks. It presents our initial experiments applying these no-GPU algorithms in pretraining and fine-tuning tasks on our CubicPower agentic mixture of experts (AMoE) framework, with the aim of contributing to more sustainable AI development. In contrast to the training procedures of neural networks, which consume significant power, the AMoE framework’s primary contribution toward power savings is that it requires no training process. We explore non-neural-network methods for solving NLP tasks and employ similarity measures to match predefined patterns for use in a RAG database.</abstract>
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%0 Conference Proceedings
%T Cubicpower Agentic Mixture of Experts(AMoE) Framework for Fine-Tuning NLP Tasks Without GPUs
%A Hsia, Chao-Yih
%Y Chang, Kai-Wei
%Y Lu, Ke-Han
%Y Yang, Chih-Kai
%Y Tam, Zhi-Rui
%Y Chang, Wen-Yu
%Y Wang, Chung-Che
%S Proceedings of the 37th Conference on Computational Linguistics and Speech Processing (ROCLING 2025)
%D 2025
%8 November
%I Association for Computational Linguistics
%C National Taiwan University, Taipei City, Taiwan
%@ 979-8-89176-379-1
%F hsia-2025-cubicpower
%X The rise of Green AI emphasizes minimizing the environmental footprint of AI systems. This paper explores a no-GPU agentic architecture for fine-tuning NLP tasks. It presents our initial experiments applying these no-GPU algorithms in pretraining and fine-tuning tasks on our CubicPower agentic mixture of experts (AMoE) framework, with the aim of contributing to more sustainable AI development. In contrast to the training procedures of neural networks, which consume significant power, the AMoE framework’s primary contribution toward power savings is that it requires no training process. We explore non-neural-network methods for solving NLP tasks and employ similarity measures to match predefined patterns for use in a RAG database.
%U https://aclanthology.org/2025.rocling-main.2/
%P 11-19

Markdown (Informal)

[Cubicpower Agentic Mixture of Experts(AMoE) Framework for Fine-Tuning NLP Tasks Without GPUs](https://aclanthology.org/2025.rocling-main.2/) (Hsia, ROCLING 2025)

• Cubicpower Agentic Mixture of Experts(AMoE) Framework for Fine-Tuning NLP Tasks Without GPUs (Hsia, ROCLING 2025)

ACL

• Chao-Yih Hsia. 2025. Cubicpower Agentic Mixture of Experts(AMoE) Framework for Fine-Tuning NLP Tasks Without GPUs. In Proceedings of the 37th Conference on Computational Linguistics and Speech Processing (ROCLING 2025), pages 11–19, National Taiwan University, Taipei City, Taiwan. Association for Computational Linguistics.