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3.3: Case Study- The Carbon Cost of Training Large Language Models

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    207227
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    Large language models (LLMs) are powerful artificial intelligence systems that can generate natural language texts for various applications, such as content generation, summarisation, and code generation. This type of AI has been thrust into the limelight by OpenAI’s ChatGPT, and they have many applications. Unfortunately, training these models requires a huge amount of computational resources and energy, which has a significant environmental impact.

    According to a study by researchers at the University of Massachusetts Amherst, training a single LLM can emit as much carbon as five cars in their lifetimes. The study estimated the energy consumption and carbon footprint of four popular LLMs: Transformer, ELMo, BERT, and GPT-2. The results showed that the most energy-intensive model was Transformer, which consumed 656,347 kWh of electricity and emitted 626,155kg of CO2 equivalent. This is equivalent to “nearly five times the lifetime emissions of the average American car”.

    Organisations like Microsoft, OpenAI, and Google are investigating ways to reduce this impact, including:


    3.3: Case Study- The Carbon Cost of Training Large Language Models is shared under a CC BY-NC 4.0 license and was authored, remixed, and/or curated by LibreTexts.

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