Title: Addressing the sustainable AI trilemma: a case study on LLM agents and RAG Show Chinese title

Title: 解决可持续AI的三难问题：大型语言模型代理与RAG的案例研究

Abstract: Large language models (LLMs) have demonstrated significant capabilities, but their widespread deployment and more advanced applications raise critical sustainability challenges, particularly in inference energy consumption. We propose the concept of the Sustainable AI Trilemma, highlighting the tensions between AI capability, digital equity, and environmental sustainability. Through a systematic case study of LLM agents and retrieval-augmented generation (RAG), we analyze the energy costs embedded in memory module designs and introduce novel metrics to quantify the trade-offs between energy consumption and system performance. Our experimental results reveal significant energy inefficiencies in current memory-augmented frameworks and demonstrate that resource-constrained environments face disproportionate efficiency penalties. Our findings challenge the prevailing LLM-centric paradigm in agent design and provide practical insights for developing more sustainable AI systems. Show Chinese abstract

Abstract: 大型语言模型（LLMs）已展现出显著的能力，但其广泛部署和更高级的应用引发了关键的可持续性挑战，尤其是在推理能耗方面。 我们提出了可持续人工智能三角困境的概念，突出了人工智能能力、数字公平性和环境可持续性之间的矛盾。 通过针对大型语言模型代理和检索增强生成（RAG）的系统案例研究，我们分析了内存模块设计中嵌入的能耗成本，并引入了新的指标来量化能耗与系统性能之间的权衡。 我们的实验结果揭示了当前增强内存框架中的显著能耗低效问题，并表明资源受限环境面临不成比例的效率惩罚。 我们的研究结果挑战了代理设计中以大型语言模型为中心的主流范式，并为开发更可持续的人工智能系统提供了实用见解。