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English(EN) The Battery Price of edge AI: A study of the Environmental Impact of LLM Inference on Mobile Devices

研究发现:智能手机上的边缘AI推理不如云端可持续

一项发表在arXiv上的新研究调查了在移动设备上运行大型语言模型(LLMs)对环境的影响,挑战了边缘AI比基于云的推理更具可持续性的假设。研究发现,与服务器批量推理相比,设备上的LLM推理的能源效率大约低三倍。此外,研究表明,考虑到完整的生命周期,本地推理并不比云推理更环保,因为设备本身的碳排放占了大部分影响。 AI

影响 挑战了本地AI更具可持续性的假设,强调了为边缘设备选择模型时需要考虑生命周期的影响。

排序理由 学术论文,详细介绍了一项关于边缘AI环境影响的研究。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

研究发现:智能手机上的边缘AI推理不如云端可持续

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学术论文,详细介绍了一项关于边缘AI环境影响的研究。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · \'Edouard Gu\'egain, Tristan Coignion ·

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