小模型能兼顾AI系统性能与能耗，助力绿色智能发展。
Balancing Sustainability And Performance: The Role Of Small-Scale LLMs In Agentic Artificial Intelligence Systems
- 用不同规模模型对比，验证小模型可降能耗
- 小模型在真实多智能体场景中保持任务质量
- 提出批量大小与资源分配的可持续设计指南
随着大语言模型在智能体系统中的广泛应用，其推理阶段的能源消耗可能带来重大可持续性挑战。本研究探讨在多智能体真实环境中，部署小型语言模型是否能在不牺牲响应速度和输出质量的前提下降低能耗。通过对比不同规模的语言模型，量化效率与性能之间的权衡关系。结果表明，小型开源权重模型可在保持任务质量的同时显著降低能源使用。基于此，我们提出了可持续人工智能设计的实用建议，包括最优批处理大小配置与计算资源分配策略。这些发现为构建可扩展、环境友好的人工智能系统提供了可操作的方法。
原文摘要 · Abstract (English)
As large language models become integral to agentic artificial intelligence systems, their energy demands during inference may pose significant sustainability challenges. This study investigates whether deploying smaller-scale language models can reduce energy consumption without compromising responsiveness and output quality in a multi-agent, real-world environments. We conduct a comparative analysis across language models of varying scales to quantify trade-offs between efficiency and performance. Results show that smaller open-weights models can lower energy usage while preserving task quality. Building on these findings, we propose practical guidelines for sustainable artificial intelligence design, including optimal batch size configuration and computation resource allocation. These insights offer actionable strategies for developing scalable, environmentally responsible artificial intelligence systems.
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