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        <title>EOP-LLM: Energy Oriented Pruning for Large Language Models</title>
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            <namePart type="given">Jiajun</namePart>
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    <abstract>In deployed large language models (LLMs), inference energy consumption has grown rapidly and has emerged as a key bottleneck in large-scale deployment, yet most existing inference efficiency methods focus on reducing FLOPs or latency, rather than explicitly modeling or enforcing end-to-end inference energy constraints. We propose EOP-LLM, an energy-oriented dynamic pruning framework that enables LLM inference under explicit per-sequence energy budgets. EOP-LLM combines a device-calibrated energy proxy with lightweight token and feed-forward (FFN) selectors, coordinated through a global dual variable, to dynamically allocate computation while preserving model quality. Extensive experiments on LLaMA 3.2 (1B/3B) and LLaMA 3.1 (8B) demonstrate that EOP-LLM consistently outperforms state-of-the-art dynamic pruning baselines under matched energy budgets, while strictly adhering to per-sequence energy constraints.</abstract>
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%0 Conference Proceedings
%T EOP-LLM: Energy Oriented Pruning for Large Language Models
%A Huang, Guan
%A Shu, Tao
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F huang-shu-2026-eop
%X In deployed large language models (LLMs), inference energy consumption has grown rapidly and has emerged as a key bottleneck in large-scale deployment, yet most existing inference efficiency methods focus on reducing FLOPs or latency, rather than explicitly modeling or enforcing end-to-end inference energy constraints. We propose EOP-LLM, an energy-oriented dynamic pruning framework that enables LLM inference under explicit per-sequence energy budgets. EOP-LLM combines a device-calibrated energy proxy with lightweight token and feed-forward (FFN) selectors, coordinated through a global dual variable, to dynamically allocate computation while preserving model quality. Extensive experiments on LLaMA 3.2 (1B/3B) and LLaMA 3.1 (8B) demonstrate that EOP-LLM consistently outperforms state-of-the-art dynamic pruning baselines under matched energy budgets, while strictly adhering to per-sequence energy constraints.
%U https://aclanthology.org/2026.findings-acl.367/
%P 7443-7464

Markdown (Informal)

[EOP-LLM: Energy Oriented Pruning for Large Language Models](https://aclanthology.org/2026.findings-acl.367/) (Huang & Shu, Findings 2026)

• EOP-LLM: Energy Oriented Pruning for Large Language Models (Huang & Shu, Findings 2026)

ACL

• Guan Huang and Tao Shu. 2026. EOP-LLM: Energy Oriented Pruning for Large Language Models. In Findings of the Association for Computational Linguistics: ACL 2026, pages 7443–7464, San Diego, California, United States. Association for Computational Linguistics.