从浮点运算到碳足迹：人工智能的资源成本

摘要

随着计算需求持续增长，评估人工智能的环境足迹需超越能源与水资源消耗范畴，纳入专用硬件的材料需求。本研究通过将计算工作量与实体硬件需求相关联，量化了AI训练的材料足迹。采用电感耦合等离子体光学发射光谱法分析英伟达A100 SXM 40GB图形处理器（GPU）的元素组成，共识别出32种元素。结果显示AI硬件约90%由重金属构成，贵金属含量仅为痕量。以质量计，铜、铁、锡、硅和镍是GPU的主要组成元素。通过多步骤研究方法，我们将这些测量数据与不同使用寿命下单个GPU的计算吞吐量相结合，并考量了不同训练效率模式下特定AI模型的训练计算需求。基于情景的分析表明：根据模型浮点运算利用率（MFU）和硬件使用寿命的差异，训练GPT-4需要1,174至8,800块A100 GPU，对应最高达7吨有毒元素的开采与最终处置。软硬件协同优化策略可降低材料需求：将MFU从20%提升至60%可使GPU需求减少67%，而将使用寿命从1年延长至3年也能实现相近的节约效果；同时实施这两项措施最高可减少93%的GPU需求。我们的研究结果揭示，诸如GPT-3.5到GPT-4所体现的渐进式性能提升，是以不成比例的高材料成本为代价的。本研究强调必须将材料资源考量纳入AI可扩展性的讨论中，明确指出未来AI发展必须符合资源效率与环境责任原则。

English

As computational demands continue to rise, assessing the environmental footprint of AI requires moving beyond energy and water consumption to include the material demands of specialized hardware. This study quantifies the material footprint of AI training by linking computational workloads to physical hardware needs. The elemental composition of the Nvidia A100 SXM 40 GB graphics processing unit (GPU) was analyzed using inductively coupled plasma optical emission spectroscopy, which identified 32 elements. The results show that AI hardware consists of about 90% heavy metals and only trace amounts of precious metals. The elements copper, iron, tin, silicon, and nickel dominate the GPU composition by mass. In a multi-step methodology, we integrate these measurements with computational throughput per GPU across varying lifespans, accounting for the computational requirements of training specific AI models at different training efficiency regimes. Scenario-based analyses reveal that, depending on Model FLOPs Utilization (MFU) and hardware lifespan, training GPT-4 requires between 1,174 and 8,800 A100 GPUs, corresponding to the extraction and eventual disposal of up to 7 tons of toxic elements. Combined software and hardware optimization strategies can reduce material demands: increasing MFU from 20% to 60% lowers GPU requirements by 67%, while extending lifespan from 1 to 3 years yields comparable savings; implementing both measures together reduces GPU needs by up to 93%. Our findings highlight that incremental performance gains, such as those observed between GPT-3.5 and GPT-4, come at disproportionately high material costs. The study underscores the necessity of incorporating material resource considerations into discussions of AI scalability, emphasizing that future progress in AI must align with principles of resource efficiency and environmental responsibility.

从浮点运算到碳足迹：人工智能的资源成本

From FLOPs to Footprints: The Resource Cost of Artificial Intelligence

摘要

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