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Greenpixie 的 AI Token 方法论：评估开放与闭源权重模型的能耗、水耗与 CO2-eq 影响
该 arXiv 论文提出一套估算云端 LLM 推理每 token 能耗的方法，区分输入（prefill）与输出（decode）token，通过基准测试实测 GPU 能耗，并用推理墙钟时间估算非 GPU 服务器能耗；采用贝叶斯线性回归建模每 token 能耗与模型规模、请求流量和硬件部署配置的关系。对规模与部署未知的闭源前沿模型，按命名约定和性能先验分入规模桶，用蒙特卡洛方法采样可能配置，给出代表性平均能耗及不确定性，并据此估算每 token 的 CO2-eq 排放（使用与隐含）和水耗。作者称该方法可为云和 SaaS 降低电费、碳排放与水耗提供可操作数据。
AI推荐理由 arXiv 预印本，作者提出一套估算云端 LLM 推理每 token 能耗的方法论：区分 prefill 与 decode token，用 GPU 实测加非 GPU 服务器能耗估算，并以贝叶斯线性回归建模能耗与模型规模、请求流量、部署配置的关系；对未知规模的闭源前沿模型按命名与性能先验分桶，用蒙特卡洛采样给出平均能耗与不确定性，并进一步估算 CO2-eq（使用与隐含）和水耗。新信息在于把开放与闭源权重模型纳入同一能耗/水/碳核算框架，对云与 SaaS 的成本与可持续性评估有实际用途；局限是闭源模型规模与部署靠先验假设，结果不确定性较大，且摘要未给出具体数值或验证结果。
72
正文
Computer Science > Computers and Society
arXiv:2609.33965 (cs)
[Submitted on 27 Sep 2026]
Title:Greenpixie's AI Token Methodology: Assessing the Energy, Water and $\mathrm{CO_2\text{-}eq}$ Impact of AI Tokens for Open and Closed Weight Models
Authors:Joshua Horswill, Ross Hunter, Matt Clifford, James Hall
View a PDF of the paper titled Greenpixie's AI Token Methodology: Assessing the Energy, Water and $\mathrm{CO_2\text{-}eq}$ Impact of AI Tokens for Open and Closed Weight Models, by Joshua Horswill and 3 other authors
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Abstract:We describe a methodology for estimating the per-token energy cost of cloud-hosted large language model (LLM) inference, separating between input (prefill) and output (decode) tokens. Graphics processing unit (GPU) energy usage is measured during inference benchmarking with open-weights models on a wide range of text-based tasks. The remaining server energy contribution from non-GPU hardware is estimated from the inference wall time. Bayesian linear regression is used to model the relationship between energy per token and LLM size, request traffic, and hardware deployment configuration. Proprietary frontier LLMs of unknown size and deployment are binned into size buckets based on naming conventions and performance priors, and the space of possible LLM configurations is sampled with Monte-Carlo methods to give a representative average energy per token and uncertainty. We also describe how these energy measurements can be used to estimate the carbon-dioxide equivalent ($\mathrm{CO_2\text{-}eq}$) emissions, both usage and embodied, and water consumed per token of AI inference. This methodology provides actionable data that enables reductions in cost, electricity usage, $\mathrm{CO_2\text{-}eq}$ emitted and water consumed in cloud and Software as a Service (SaaS).
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Computers and Society (cs.CY); Machine Learning (cs.LG)
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arXiv:2609.33965 [cs.CY]
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arXiv:2609.33965v1 [cs.CY] for this version)
https://doi.org/10.48550/arXiv.2609.33965
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From: Ross Hunter [view email]
[v1]
Sun, 27 Sep 2026 22:10:47 UTC (339 KB)
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View a PDF of the paper titled Greenpixie's AI Token Methodology: Assessing the Energy, Water and $\mathrm{CO_2\text{-}eq}$ Impact of AI Tokens for Open and Closed Weight Models, by Joshua Horswill and 3 other authors
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