Green LLMs

The exponential rise of Large Language Models (LLMs) like GPT, LLaMA, and Gemini has transformed industries such as healthcare, education, and governance but at the cost of massive energy consumption and carbon emissions. This chapter introduces the concept of Green LLMs, emphasizing sustainable design, efficient deployment, and governance frameworks. Technical strategies such as pruning, quantization, knowledge distillation, sparse transformers, and mixed-precision computing are explored alongside deployment practices like adaptive inference and on-device integration. Ethical imperatives and regulatory frameworks are highlighted, promoting carbon accounting and sustainability benchmarks. Case studies from OpenAI and DeepMind demonstrate significant efficiency gains with minimal accuracy loss, offering proof-of-concept for balancing innovation with ecological responsibility. By integrating technical, ethical, and policy dimensions, the chapter presents a roadmap for developing powerful, energy-efficient, and environmentally accountable LLMs aligned with global climate goals.