| HERO_TEXT = """ | |
| <div align="center"> | |
| <a href="https://ecologits.ai/"> | |
| <img style="max-height: 80px" alt="EcoLogits" src="https://raw.githubusercontent.com/genai-impact/ecologits/main/docs/assets/logo_light.png"> | |
| </a> | |
| </div> | |
| <h1 align="center">🧮 EcoLogits Calculator</h1> | |
| <div align="center"> | |
| <p style="max-width: 500px; text-align: center"> | |
| <i><b>EcoLogits</b> is a python library that tracks the <b>energy consumption</b> and <b>environmental | |
| footprint</b> of using <b>generative AI</b> models through APIs.</i> | |
| </p> | |
| </div> | |
| <br> | |
| """ | |
| INTRO_TEXT = """ | |
| This tool is developed and maintained by [GenAI Impact](https://genai-impact.org/) non-profit. Learn more about | |
| 🌱 EcoLogits by reading the documentation on [ecologits.ai](https://ecologits.ai). | |
| 🩷 Support us by giving a ⭐️ on our [GitHub repository](https://github.com/genai-impact/ecologits) and by following our [LinkedIn page](https://www.linkedin.com/company/genai-impact/). | |
| """ | |
| WARNING_CLOSED_SOURCE = """ | |
| ⚠️ The model architecture has not been publicly released, expect lower precision of estimations. | |
| """ | |
| WARNING_MULTI_MODAL = """ | |
| ⚠️ The model architecture is multimodal, expect lower precision of estimations. | |
| """ | |
| WARNING_BOTH = """ | |
| ⚠️ The model architecture has not been publicly released and is multimodal, expect lower precision of estimations. | |
| """ | |
| ABOUT_TEXT = r""" | |
| ## 🎯 Our goal | |
| **The main goal of the EcoLogits Calculator is to raise awareness on the environmental impacts of LLM inference.** | |
| The rapid evolution of generative AI is reshaping numerous industries and aspects of our daily lives. While these | |
| advancements offer some benefits, they also **pose substantial environmental challenges that cannot be overlooked**. | |
| Plus the issue of AI's environmental footprint has been mainly discussed at training stage but rarely at the inference | |
| stage. That is an issue because **inference impacts for LLMs can largely overcome the training impacts when deployed | |
| at large scales**. | |
| At **[GenAI Impact](https://genai-impact.org/) we are dedicated to understanding and mitigating the environmental | |
| impacts of generative AI** through rigorous research, innovative tools, and community engagement. Especially, in early | |
| 2024 we have launched an new open-source tool called [EcoLogits](https://github.com/genai-impact/ecologits) that tracks | |
| the energy consumption and environmental footprint of using generative AI models through APIs. | |
| ## 🙋 FAQ | |
| **How we assess the impacts of closed-source models?** | |
| Environmental impacts are calculated based on model architecture and parameter count. For closed-source models, we | |
| lack transparency from providers, so we estimate parameter counts using available information. For GPT models, we | |
| based our estimates on leaked GPT-4 architecture and scaled parameters count for GPT-4-Turbo and GPT-4o based on | |
| pricing differences. For other proprietary models like Anthropic's Claude, we assume similar impacts for models | |
| released around the same time with similar performance on public benchmarks. Please note that these estimates are | |
| based on assumptions and may not be exact. Our methods are open-source and transparent so you can always see the | |
| hypotheses we use. | |
| **Which generative AI models or providers are supported?** | |
| To see the full list of **generative AI providers** currently supported by EcoLogits, see the following | |
| [documentation page](https://ecologits.ai/providers/). As of today we only support LLMs but we plan to add support for | |
| embeddings, image generation, multi-modal models and more. If you are interested don't hesitate to | |
| [join us](https://genai-impact.org/contact/) and accelerate our work! | |
| **How to reduce AI environmental impacts?** | |
| * Look at **indirect impacts** of your project. Does the finality of your project is impacting negatively the | |
| environment? | |
| * **Be frugal** and question your usage or need of AI | |
| * Do you really need AI to solve your problem? | |
| * Do you really need GenAI to solve your problem? (you can read this [paper](https://aclanthology.org/2023.emnlp-industry.39.pdf)) | |
| * Use small and specialized models to solve your problem. | |
| * Evaluate before, during and after the development of your project the environmental impacts with tools like | |
| 🌱 [EcoLogits](https://github.com/genai-impact/ecologits) or [CodeCarbon](https://github.com/mlco2/codecarbon) | |
| (see [more tools](https://github.com/samuelrince/awesome-green-ai)) | |
| * Restrict the use case and limit the usage of your tool or feature to the desired purpose. | |
| * Do NOT buy new GPUs / hardware | |
| * Hardware manufacturing for data centers is around 50% of the impact. | |
| * Use cloud instances that are located in low emissions / high energy efficiency data centers | |
| (see [electricitymaps.com](https://app.electricitymaps.com/map)) | |
| * Optimize your models for production | |
| * Quantize your models. | |
| * Use inference optimization tricks. | |
| * Prefer fine-tuning of small and existing models over generalist models. | |
| **What is the difference between **EcoLogits** and [CodeCarbon](https://github.com/mlco2/codecarbon)?** | |
| EcoLogits is focused on estimating the environmental impacts of generative AI (only LLMs for now) used **through API | |
| providers (such as OpenAI, Anthropic, Cloud APIs...)** whereas CodeCarbon is more general tool to measure energy | |
| consumption and estimate GHG emissions measurement. If you deploy LLMs locally we encourage you to use CodeCarbon to | |
| get real numbers of your energy consumption. | |
| ## 🤗 Contributing | |
| We are eager to get feedback from the community, don't hesitate to engage the discussion with us on this | |
| [GitHub thread](https://github.com/genai-impact/ecologits/discussions/45) or message us on | |
| [LinkedIn](https://www.linkedin.com/company/genai-impact/). | |
| We also welcome any open-source contributions on 🌱 **[EcoLogits](https://github.com/genai-impact/ecologits)** or on | |
| 🧮 **EcoLogits Calculator**. | |
| ## ⚖️ License | |
| <p xmlns:cc="http://creativecommons.org/ns#" > | |
| This work is licensed under | |
| <a href="https://creativecommons.org/licenses/by-sa/4.0/?ref=chooser-v1" target="_blank" rel="license noopener noreferrer" style="display:inline-block;"> | |
| CC BY-SA 4.0 | |
| </a> | |
| <img style="display:inline-block;height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/cc.svg?ref=chooser-v1" alt=""> | |
| <img style="display:inline-block;height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/by.svg?ref=chooser-v1" alt=""> | |
| <img style="display:inline-block;height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/sa.svg?ref=chooser-v1" alt=""> | |
| </p> | |
| ## 🙌 Acknowledgement | |
| We thank [Data For Good](https://dataforgood.fr/) and [Boavizta](https://boavizta.org/en) for supporting the | |
| development of this project. Their contributions of tools, best practices, and expertise in environmental impact | |
| assessment have been invaluable. | |
| We also extend our gratitude to the open-source contributions of 🤗 [Hugging Face](huggingface.com) on the LLM-Perf | |
| Leaderboard. | |
| ## 🤝 Contact | |
| For general question on the project, please use the [GitHub thread](https://github.com/genai-impact/ecologits/discussions/45). | |
| Otherwise use our contact form on [genai-impact.org/contact](https://genai-impact.org/contact/). | |
| """ | |
| METHODOLOGY_TEXT = r""" | |
| ## 📖 Methodology | |
| We have developed a methodology to **estimate the energy consumption and environmental impacts for an LLM inference** | |
| based on request parameters and hypotheses on the data center location, the hardware used, the model architecture and | |
| more. | |
| In this section we will only cover the principles of the methodology related to the 🧮 **EcoLogits Calculator**. If | |
| you wish to learn more on the environmental impacts modeling of an LLM request checkout the | |
| 🌱 [EcoLogits documentation page](https://ecologits.ai/methodology/). | |
| ### Modeling impacts of an LLM request | |
| The environmental impacts of an LLM inference are split into the **usage impacts** $I_{request}^u$ to account for | |
| electricity consumption and the **embodied impacts** $I_{request}^e$ that relates to resource extraction, hardware | |
| manufacturing and transportation. In general terms it can be expressed as follow: | |
| $$ I_{request} = I_{request}^u + I_{request}^e $$ | |
| $$ I_{request} = E_{request}*F_{em}+\frac{\Delta T}{\Delta L}*I_{server}^e $$ | |
| With, | |
| * $E_{request}$ the estimated energy consumption of the server and its cooling system. | |
| * $F_{em}$ the electricity mix that depends on the country and time. | |
| * $\frac{\Delta T}{\Delta L}$ the hardware usage ratio i.e. the computation time over the lifetime of the hardware. | |
| * $I_{server}^e$ the embodied impacts of the server. | |
| Additionally, to ⚡️ **direct energy consumption** the environmental impacts are expressed in **three dimensions | |
| (multi-criteria impacts)** that are: | |
| * 🌍 **Global Warming Potential** (GWP): Potential impact on global warming in kgCO2eq (commonly known as GHG/carbon | |
| emissions). | |
| * 🪨 **Abiotic Depletion Potential for Elements** (ADPe): Impact on the depletion of non-living resources such as | |
| minerals or metals in kgSbeq. | |
| * ⛽️ **Primary Energy** (PE): Total energy consumed from primary sources in MJ. | |
| ### Principles, Data and Hypotheses | |
| We use a **bottom-up methodology** to model impacts, meaning that we will estimate the impacts of low-level physical | |
| components to then estimate the impacts at software level (in that case an LLM inference). We also rely on **Life | |
| Cycle Approach (LCA) proxies and approach** to model both usage and embodied phases with multi-criteria impacts. | |
| If you are interested in this approach we recommend you to read the following [Boavizta](https://boavizta.org/) | |
| resources. | |
| * [Digital & environment: How to evaluate server manufacturing footprint, beyond greenhouse gas emissions?](https://boavizta.org/en/blog/empreinte-de-la-fabrication-d-un-serveur) | |
| * [Boavizta API automated evaluation of environmental impacts of ICT services and equipments](https://boavizta.org/en/blog/boavizta-api-automated-evaluation-of-ict-impacts-on-the-environment) | |
| * [Boavizta API documentation](https://doc.api.boavizta.org/) | |
| We leverage **open data to estimate the environmental impacts**, here is an exhaustive list of our data providers. | |
| * [LLM-Perf Leaderboard](https://huggingface.co/spaces/optimum/llm-perf-leaderboard) to estimate GPU energy consumption | |
| and latency based on the model architecture and number of output tokens. | |
| * [Boavizta API](https://github.com/Boavizta/boaviztapi) to estimate server embodied impacts and base energy | |
| consumption. | |
| * [ADEME Base Empreinte®](https://base-empreinte.ademe.fr/) for electricity mix impacts per country. | |
| Finally here are the **main hypotheses** we have made to compute the impacts. | |
| * ⚠️ **We *"guesstimate"* the model architecture of proprietary LLMs when not disclosed by the provider.** | |
| * Production setup: quantized models running on data center grade servers and GPUs such as A100. | |
| * Electricity mix does not depend on time (help us enhance EcoLogits and work on this [issue](https://github.com/genai-impact/ecologits/issues/42)) | |
| * Ignore the following impacts: unused cloud resources, data center building, network and end-user devices... (for now) | |
| ## Equivalents | |
| We have integrated impact equivalents to help people better understand the impacts and have reference points for | |
| standard use cases and everyday activities. | |
| ### Request impacts | |
| These equivalents are computed based on the request impacts only. | |
| #### 🚶♂️➡️ Walking or 🏃♂️➡️ running distance | |
| We compare the ⚡️ direct energy consumption with the energy consumption of someone 🚶♂️➡️ walking or 🏃♂️➡️ running. | |
| From [runningtools.com](https://www.runningtools.com/energyusage.htm) we consider the following energy values per | |
| physical activity (for someone weighing 70kg): | |
| * 🚶♂️➡️ walking: $ 196\ kJ/km $ (speed of $ 3\ km/h $) | |
| * 🏃♂️➡️ running: $ 294\ kJ/km $ (speed of $ 10\ km/h $) | |
| We divide the request energy consumption by these values to compute the distance traveled. | |
| #### 🔋 Electric Vehicle distance | |
| We compare the ⚡️ direct energy consumption with the energy consumer by a EV car. From | |
| [selectra.info](https://selectra.info/energie/actualites/insolite/consommation-vehicules-electriques-france-2040) or | |
| [tesla.com](https://www.tesla.com/fr_fr/support/power-consumption) we consider an average value of energy consumed per | |
| kilometer of: $ 0.17\ kWh/km $. | |
| We divide the request energy consumption by this value to compute the distance driven by an EV. | |
| #### ⏯️ Streaming time | |
| We compare the 🌍 GHG emissions of the request and of streaming a video. From | |
| [impactco2.fr](https://impactco2.fr/outils/comparateur?value=1&comparisons=streamingvideo), we consider that | |
| $ 1\ kgCO2eq $ is equivalent to $ 15.6\ h $ of streaming. | |
| We multiply that value by the GHG emissions of the request to get an equivalent in hours of video streaming. | |
| ### Scaled impacts | |
| These equivalents are computed based on the request impacts scaled to a worldwide adoption use case. We imply that the | |
| same request is done 1% of the planet everyday for 1 year, and then compute impact equivalents. | |
| $$ | |
| I_{scaled} = I_{request} * [1 \\% \ \text{of}\ 8B\ \text{people on earth}] * 365\ \text{days} | |
| $$ | |
| #### Number of 💨 wind turbines or ☢️ nuclear plants | |
| We compare the ⚡️ direct energy consumption (scaled) by the energy production of wind turbines and nuclear power | |
| plants. From [ecologie.gouv.fr](https://www.ecologie.gouv.fr/eolien-terrestre) we consider that a $ 2\ MW $ wind | |
| turbine produces $ 4.2\ GWh $ a year. And from [edf.fr](https://www.edf.fr/groupe-edf/espaces-dedies/jeunes-enseignants/pour-les-jeunes/lenergie-de-a-a-z/produire-de-lelectricite/le-nucleaire-en-chiffres) | |
| we learn that a $ 900\ MW $ nuclear power plant produces $ 6\ TWh $ a year. | |
| We divide the scaled energy consumption by these values to get the number of wind turbines or nuclear power plants | |
| needed. | |
| #### Multiplier of 🇮🇪 Ireland electricity consumption | |
| We compare the ⚡️ direct energy consumption (scaled) by the electricity consumption of Ireland per year. From | |
| [wikipedia.org](https://en.wikipedia.org/wiki/List_of_countries_by_electricity_consumption) we consider the Ireland | |
| electricity consumption to be $ 33\ TWh $ a year for a population of 5M. | |
| We divide the scaled energy consumption by this value to get the equivalent number of "Ireland countries". | |
| #### Number of ✈️ Paris ↔ New York City flights | |
| We compare the 🌍 GHG emissions (scaled) of the request and of a return flight Paris ↔ New York City. From | |
| [impactco2.fr](https://impactco2.fr/outils/comparateur?value=1&comparisons=&equivalent=avion-pny) we consider that a | |
| return flight Paris → New York City → Paris for one passenger emits $ 1,770\ kgCO2eq $ and we consider an overall | |
| average load of 100 passengers per flight. | |
| We divide the scaled GHG emissions by this value to get the equivalent number of return flights. | |
| #### If you are motivated to help us test and enhance this methodology [contact us](https://genai-impact.org/contact/)! 💪 | |
| """ | |
| CITATION_LABEL = "BibTeX citation for EcoLogits Calculator and the EcoLogits library:" | |
| CITATION_TEXT = r"""@misc{ecologits-calculator, | |
| author={Samuel Rincé, Adrien Banse and Valentin Defour}, | |
| title={EcoLogits Calculator}, | |
| year={2025}, | |
| howpublished= {\url{https://huggingface.co/spaces/genai-impact/ecologits-calculator}}, | |
| } | |
| @software{ecologits, | |
| author = {Samuel Rincé, Adrien Banse, Vinh Nguyen and Luc Berton}, | |
| publisher = {GenAI Impact}, | |
| title = {EcoLogits: track the energy consumption and environmental footprint of using generative AI models through APIs.}, | |
| }""" | |
| LICENCE_TEXT = """<p xmlns:cc="http://creativecommons.org/ns#" > | |
| This work is licensed under | |
| <a href="https://creativecommons.org/licenses/by-sa/4.0/?ref=chooser-v1" target="_blank" rel="license noopener noreferrer" style="display:inline-block;"> | |
| CC BY-SA 4.0 | |
| </a> | |
| <img style="display:inline-block;height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/cc.svg?ref=chooser-v1" alt=""> | |
| <img style="display:inline-block;height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/by.svg?ref=chooser-v1" alt=""> | |
| <img style="display:inline-block;height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/sa.svg?ref=chooser-v1" alt=""> | |
| </p>""" |