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LLMCarbon

A prelimiary code repo for LLMCarbon: Modeling the End-to-End Carbon Footprint of Large Language Models. More details can be viewed at https://github.com/UnchartedRLab/LLMCarbon. LLMCarbon provides precise predictions of both operational and embodied carbon footprints of large language models (LLMs), enabling effective exploration of the design space by considering the trade-off between test loss and carbon footprint. These carbon footprint exploration can be considered before training an LLM to ensure responsible and sustainable development.

Run Validations

To generate the data in the table 4 and table 5 in the paper

python3 llmcarbon_tutorial.py

Estimation of CO2 equivalent emissions of tranformer based large language models

Estimated regression coefficients used for polynomial fit $\mathbf{y = ax^2 + bx + c} $

Tensor model throughput: $$a= -8.82079068\times 10^{-20}, b= 1.68591116\times 10^{-09}, c= 1.33954735\times 10^{+02}$$
Pipeline model throughput: $$a= -5.60233749\times 10^{-23}, b= 8.45435587\times 10^{-11}, c= 1.34546129\times 10^{+02}$$
Total number of GPUs: $$a= -2.12910565\times 10^{-21}, b= 4.39684339\times 10^{-09}, c=7.99173057\times 10^{+02}$$
Batch Size: $$a = -4.29439186\times 10^{-01}, b= 5.21376002\times 10^{+01}, c= 1.43737095\times 10^{+03}$$

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Bibtex

@inproceedings{
faiz2024llmcarbon,
title={{LLMC}arbon: Modeling the End-to-End Carbon Footprint of Large Language Models},
author={Ahmad Faiz and Sotaro Kaneda and Ruhan Wang and Rita Chukwunyere Osi and Prateek Sharma and Fan Chen and Lei Jiang},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=aIok3ZD9to}
}