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Research paperPDF

International Conference on Learning Representations (ICLR 2024) 2024

LLMCarbon: Modeling the End-to-End Carbon Footprint of Large Language Models

In AI data-centre carbon estimation, compute operational (FLOPs, power, PUE, grid intensity) and embodied (chip area × CPA, memory and storage per GB) carbon together; in a renewable-powered facility embodied carbon rises to 92-95%.

  • Against published Google and Meta figures, error is 8.2% or less for training and -3.05% for embodied carbon.

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Research paperPDF

arXiv preprint (math.OC), MIT Operations Research Center 2025

Locational Marginal Emissions for Carbon-Aware Data Center Operations in Large-Scale Power Grids

In data-centre carbon accounting, consider hourly locational marginal emissions (LME) rather than an annual-average grid factor; the LME-based intensity (about 672 kgCO2/MWh) was about 3 times the average factor (202).

  • Emission rates assumed in the simulated grid: coal 800 kgCO2/MWh, gas 450 kgCO2/MWh, wind/solar/nuclear/hydro 0.

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