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LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models (2309.14393v2)

Published 25 Sep 2023 in cs.CL, cs.AI, cs.CY, and cs.LG

Abstract: The carbon footprint associated with LLMs is a significant concern, encompassing emissions from their training, inference, experimentation, and storage processes, including operational and embodied carbon emissions. An essential aspect is accurately estimating the carbon impact of emerging LLMs even before their training, which heavily relies on GPU usage. Existing studies have reported the carbon footprint of LLM training, but only one tool, mlco2, can predict the carbon footprint of new neural networks prior to physical training. However, mlco2 has several serious limitations. It cannot extend its estimation to dense or mixture-of-experts (MoE) LLMs, disregards critical architectural parameters, focuses solely on GPUs, and cannot model embodied carbon footprints. Addressing these gaps, we introduce \textit{\carb}, an end-to-end carbon footprint projection model designed for both dense and MoE LLMs. Compared to mlco2, \carb~significantly enhances the accuracy of carbon footprint estimations for various LLMs. The source code is released at \url{https://github.com/SotaroKaneda/MLCarbon}.

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Citations (39)
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Summary

  • The paper introduces LLMCarbon, a comprehensive methodology for estimating carbon emissions across all phases of an LLM's lifecycle.
  • It presents a hardware efficiency model that deduces optimal data, tensor, pipeline, and expert configurations to minimize emissions.
  • Validation against models like T5 and GPT-3 shows projections within an 8.2% discrepancy, underscoring its practical impact in sustainable AI.

An Expert Examination of LLMCarbon: An End-to-End Model for Estimating the Carbon Footprint of LLMs

The environmental impact of machine learning, specifically from LLMs, necessitates comprehensive models to predict and assess carbon emissions. The paper, "LLMCarbon: Modeling the End-to-End Carbon Footprint of LLMs," addresses this by proposing LLMCarbon, a model that surpasses existing tools in accurately projecting the carbon footprint across various phases of an LLM’s lifecycle, including training, inference, experimentation, and storage. This document scrutinizes the components and utility of LLMCarbon against past endeavors, the theoretical implications, and potential contributions to the field.

Technical Evaluation

Previous attempts to gauge the carbon footprint, such as the tool mlco2, have focused primarily on operational emissions during the training phase, relying heavily on GPU utilization and oversimplified assumptions. LLMCarbon attempts to rectify these inaccuracies by incorporating a more exhaustive set of parameters, accounting for both the operational and embodied carbon footprints. Specifically, LLMCarbon processes vital elements like LLM architectural details, hardware configurations, and data center efficiencies. It takes into consideration not only conventional GPU usage but expanded configurations including TPUs, thus allowing for MoE models, which present a more nuanced challenge due to their sparse architecture.

One of the paper's pivotal contributions is the hardware efficiency model, which deduces optimal configurations for data, tensor, pipeline, and expert parallelism. This furnishes users with the ability to significantly reduce the carbon emissions of LLMs when trained under non-optimal settings.

Validation and Challenges

Through validation against well-acknowledged LLMs such as T5 by Google and GPT-3 by OpenAI, LLMCarbon's projections align closely with published carbon footprint data, achieving a discrepancy of ≤8.2%\leq8.2\%. This close alignment represents a significant improvement over previous models. However, when faced with predicting the operational footprint during the training of MoE models, the tool’s margin of error increases, signaling room for further refinement, particularly concerning complex MoE architectures.

Implications and Future Directions

The implications of LLMCarbon are multifaceted. Practically, it allows data centers and developers to devise intelligent trade-offs between carbon attributes and model performance, potentially guiding the choice of hardware or advocating for energy-efficient practices. Theoretically, this work underscores the importance of integrating embodied carbon metrics—a previously underexplored dimension—into machine learning lifecycle assessments.

Moreover, while LLMCarbon sets a robust foundation, future developments could explore real-time carbon tracking and incorporate dynamic workload changes, which may affect carbon output during various phases of the ML lifecycle. Additionally, extending LLMCarbon's applicability to encompass a wider array of hardware interfaces and emergent architectures like neuromorphic computing could further its impact.

Concluding Thoughts

"LLMCarbon: Modeling the End-To-End Carbon Footprint of LLMs" is a methodologically rigorous attempt to tackle the carbon footprint challenge in AI's rapidly expanding field. By straddling practical implementation and theoretical innovation, it significantly contributes to recognizing and optimizing the environmental ramifications of large-scale AI deployments. Future research leveraging LLMCarbon could further sustainable computing efforts, encompassing comprehensive assessments of not just ML systems but an increasingly digitized global ecosystem.

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  1. GitHub - SotaroKaneda/MLCarbon: End-to-end carbon footprint mod- eling tool (30 stars)
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