让数据中心供电像计算一样动态响应，提升能效与可持续性。
Improving AI Efficiency in Data Centres by Power Dynamic Response
- 将输入电力动态调节，匹配计算功耗变化
- 实测提升能效，降低资本与管理成本
- 适合关注绿色算力的超大规模数据中心
近年来，人工智能（AI）的快速发展得益于大语言模型和基础模型等复杂模型的出现。确保稳健可靠的电源基础设施，是充分发挥AI潜力的关键。然而，AI数据中心对电力需求巨大，其能源管理问题日益突出，尤其在环境与可持续发展方面。本文研究了一种创新的电源管理方案：使部分输入电力随数据计算功耗动态调整。通过分析全球多个数据平台的电源趋势，量化并比较了被动与主动设备在计算增益、能效、资本支出和管理成本方面的表现。该策略标志着AI数据中心电源管理范式转变，有望显著提升超大规模AI企业对环境、财务和社会的影响。
原文摘要 · Abstract (English)
The steady growth of artificial intelligence (AI) has accelerated in the recent years, facilitated by the development of sophisticated models such as large language models and foundation models. Ensuring robust and reliable power infrastructures is fundamental to take advantage of the full potential of AI. However, AI data centres are extremely hungry for power, putting the problem of their power management in the spotlight, especially with respect to their impact on environment and sustainable development. In this work, we investigate the capacity and limits of solutions based on an innovative approach for the power management of AI data centres, i.e., making part of the input power as dynamic as the power used for data-computing functions. The performance of passive and active devices are quantified and compared in terms of computational gain, energy efficiency, reduction of capital expenditure, and management costs by analysing power trends from multiple data platforms worldwide. This strategy, which identifies a paradigm shift in the AI data centre power management, has the potential to strongly improve the sustainability of AI hyperscalers, enhancing their footprint on environmental, financial, and societal fields.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。