The launch of OpenAI's ChatGPT highlighted both the exciting potential of generative AI and the environmental risks associated with using Large Language Models (LLMs). Training these models, such as GPT-3, requires extensive energy and computational resources, emphasizing the importance of sustainable practices in AI development. The paper questions whether LLMs can optimize their processes to reduce energy consumption and ultimately lower their environmental impact. With billions of queries made daily, addressing these energy challenges is crucial for the continued evolution and acceptance of AI technologies in various sectors.
When OpenAI launched ChatGPT in late 2022, it sparked both delight and concern. Generative AI demonstrated remarkable potential-crafting essays, solving coding problems, and even creating art. But it also raised alarms among environmentalists, researchers, and technologists.
The biggest concern? The massive energy consumption required to train and run Large Language Models (LLMs), prompting questions about their long-term sustainability.
Even after training, the inference phase-where models handle real-world tasks-adds to energy use. Although the energy required for a single query is small, when we consider that there are billions of such interactions taking place across various platforms every day, it becomes a significant problem.
The carbon footprint depends on the energy mix powering data centers. The energy challenges of LLMs, from training to inference, are significant and require innovative solutions.
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