Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
38 tokens/sec
GPT-4o
59 tokens/sec
Gemini 2.5 Pro Pro
41 tokens/sec
o3 Pro
7 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

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}.

PDF HTML Abstract

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.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown Bookmark Chat Bubble Oval Streamline Icon: https://streamlinehq.com Chat (Pro)
Definition Search Book Streamline Icon: https://streamlinehq.com
References (52)
  1. Carbon explorer: A holistic framework for designing carbon aware datacenters. In ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2, pp.  118–132, 2023.
  2. Palm 2 technical report. arXiv preprint arXiv:2305.10403, 2023.
  3. Carbontracker: Tracking and predicting the carbon footprint of training deep learning models. arXiv preprint arXiv:2007.03051, 2020.
  4. Efficient large scale language modeling with mixtures of experts. arXiv preprint arXiv:2112.10684, 2021.
  5. Green cloud computing: Balancing energy in processing, storage, and transport. Proceedings of the IEEE, 99(1):149–167, 2011.
  6. Language models are few-shot learners. In Advances in Neural Information Processing Systems, volume 33, pp.  1877–1901, 2020.
  7. Broken neural scaling laws. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=sckjveqlCZ.
  8. Are the new ais smart enough to steal your job? iq scores for chatgpt, microsoft bing, google bard and quora poe. IQ Scores for ChatGPT, Microsoft Bing, Google Bard and Quora Poe (April 7, 2023), 2023.
  9. Pipeline moe: A flexible moe implementation with pipeline parallelism. arXiv preprint arXiv:2304.11414, 2023.
  10. Jeongdong Choe. Memory technology 2021: Trends & challenges. In 2021 International Conference on Simulation of Semiconductor Processes and Devices (SISPAD), pp.  111–115. IEEE, 2021.
  11. Palm: Scaling language modeling with pathways. arXiv preprint arXiv:2204.02311, 2022.
  12. Unsupervised cross-lingual representation learning at scale. In Annual Meeting of the Association for Computational Linguistics, pp.  8440–8451, July 2020.
  13. Measuring the carbon intensity of ai in cloud instances. In ACM Conference on Fairness, Accountability, and Transparency, pp.  1877––1894, New York, NY, USA, 2022. Association for Computing Machinery. ISBN 9781450393522.
  14. Glam: Efficient scaling of language models with mixture-of-experts. In International Conference on Machine Learning, pp.  5547–5569. PMLR, 2022.
  15. Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity. The Journal of Machine Learning Research, 23(1):5232–5270, 2022.
  16. Dtco including sustainability: Power-performance-area-cost-environmental score (ppace) analysis for logic technologies. In IEEE International Electron Devices Meeting, pp.  41.4.1–41.4.4, 2020.
  17. Chasing carbon: The elusive environmental footprint of computing. IEEE Micro, 42(4):37––47, jul 2022.
  18. Towards the systematic reporting of the energy and carbon footprints of machine learning. Journal of Machine Learning Research, 21(1), jan 2020. ISSN 1532-4435.
  19. Training compute-optimal large language models. arXiv preprint arXiv:2203.15556, 2022.
  20. In-datacenter performance analysis of a tensor processing unit. In IEEE/ACM International symposium on computer architecture, pp.  1–12, 2017.
  21. Scaling laws for neural language models. arXiv preprint arXiv:2001.08361, 2020.
  22. Scalable and efficient moe training for multitask multilingual models. arXiv preprint arXiv:2109.10465, 2021.
  23. Quantifying the carbon emissions of machine learning. arXiv preprint arXiv:1910.09700, 2019.
  24. A holistic assessment of the carbon footprint of noor, a very large Arabic language model. In Proceedings of BigScience Episode #5 – Workshop on Challenges & Perspectives in Creating Large Language Models, pp.  84–94, may 2022.
  25. Gshard: Scaling giant models with conditional computation and automatic sharding. In International Conference on Learning Representations, 2021. URL https://openreview.net/forum?id=qrwe7XHTmYb.
  26. Jurassic-1: Technical details and evaluation. White Paper. AI21 Labs, 1, 2021.
  27. Energy consumption and emission mitigation prediction based on data center traffic and pue for global data centers. Global Energy Interconnection, 3(3):272–282, 2020.
  28. Efficient large-scale language model training on gpu clusters using megatron-lm. In ACM International Conference for High Performance Computing, Networking, Storage and Analysis, 2021.
  29. Carbon emissions and large neural network training. arXiv preprint arXiv:2104.10350, 2021.
  30. The carbon footprint of machine learning training will plateau, then shrink. Computer, 55(7):18–28, 2022.
  31. The carbon footprint of distributed cloud storage. arXiv preprint arXiv:1803.06973, 2018.
  32. Scaling language models: Methods, analysis & insights from training gopher. arXiv preprint arXiv:2112.11446, 2021.
  33. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(140):1–67, 2020. URL http://jmlr.org/papers/v21/20-074.html.
  34. Deepspeed-moe: Advancing mixture-of-experts inference and training to power next-generation ai scale. In International Conference on Machine Learning, pp.  18332–18346, 2022.
  35. Katharine Sanderson. Gpt-4 is here: what scientists think. Nature, 615(7954):773, 2023.
  36. Bloom: A 176b-parameter open-access multilingual language model. arXiv preprint arXiv:2211.05100, 2022.
  37. Green ai. Communications of the ACM, 63(12):54––63, nov 2020.
  38. zen 2: The amd 7nm energy-efficient high-performance x86-64 microprocessor core. In 2020 IEEE International Solid-State Circuits Conference-(ISSCC), pp.  42–44. IEEE, 2020.
  39. Using deepspeed and megatron to train megatron-turing nlg 530b, a large-scale generative language model. arXiv preprint arXiv:2201.11990, 2022.
  40. Energy and policy considerations for deep learning in nlp. In Annual Meeting of the Association for Computational Linguistics, pp.  3645–3650, 2019.
  41. The dirty secret of ssds: Embodied carbon. In The 1st Workshop on Sustainable Computer Systems Design and Implementation, 2022.
  42. Deep learning’s diminishing returns: The cost of improvement is becoming unsustainable. IEEE Spectrum, 58(10):50–55, 2021. doi: 10.1109/MSPEC.2021.9563954.
  43. Lamda: Language models for dialog applications. arXiv preprint arXiv:2201.08239, 2022.
  44. TSMC. TSMC Corporate Social Responsibility Report. https://esg.tsmc.com/download/file/2019-csr-report/english/pdf/e-all.pdf, 2019.
  45. Wiki. Ampere (microarchitecture). http://en.wikipedia.org/w/index.php?title=Ampere%20(microarchitecture)&oldid=1160464393, 2023a.
  46. Wiki. Tensor Processing Unit. http://en.wikipedia.org/w/index.php?title=Tensor%20Processing%20Unit&oldid=1158650479, 2023b.
  47. Sustainable ai: Environmental implications, challenges and opportunities. Proceedings of Machine Learning and Systems, 4:795–813, 2022.
  48. Petuum: A new platform for distributed machine learning on big data. IEEE Transactions on Big Data, 1(2):49–67, 2015.
  49. Yandex. Yalm 100b. https://github.com/yandex/YaLM-100B, 2022.
  50. Orca: A distributed serving system for {{\{{Transformer-Based}}\}} generative models. In USENIX Symposium on Operating Systems Design and Implementation, pp.  521–538, 2022.
  51. GLM-130b: An open bilingual pre-trained model. In The Eleventh International Conference on Learning Representations, 2023.
  52. St-moe: Designing stable and transferable sparse expert models. arXiv preprint arXiv:2202.08906, 2022.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (7)
  1. Ahmad Faiz (2 papers)
  2. Sotaro Kaneda (1 paper)
  3. Ruhan Wang (9 papers)
  4. Rita Osi (1 paper)
  5. Fan Chen (85 papers)
  6. Lei Jiang (85 papers)
  7. Prateek Sharma (90 papers)
Citations (39)
View on Semantic Scholar
Github Logo Streamline Icon: https://streamlinehq.com

GitHub

  1. GitHub - SotaroKaneda/MLCarbon: End-to-end carbon footprint mod- eling tool (30 stars)
Youtube Logo Streamline Icon: https://streamlinehq.com

YouTube