Green AI: Model Compression and Efficient LLM Deployment

As artificial intelligence and large language models scale rapidly, the energy consumption and computational costs of running these systems have reached critical levels. Building smart AI is no longer just about predictive accuracy; it is about efficiency and environmental responsibility. This text-based course guides you through the essential methodologies of Green AI, helping you transition from understanding basic carbon impacts to implementing advanced optimization techniques that make your systems faster, lighter, and more sustainable. What you'll learn: - Understand the foundational concepts of Green AI, including carbon tracking and computational efficiency metrics. - Apply model compression techniques such as weight pruning and knowledge distillation to reduce model size. - Implement low-bit quantization strategies to run large language models on resource-constrained hardware. - Explore collaborative inference architectures to optimize model execution across distributed environments. - Analyze modern open-source tools and frameworks designed for sustainable AI deployment. - Evaluate the trade-offs between model performance, accuracy, and energy consumption. This course begins with key terminology, basic concepts, and foundational definitions of sustainable computing. From there, you will progress through clear, written explanations of optimization methodologies, practical code snippets, and conceptual exercises designed to solidify your understanding. It is ideal for developers, data scientists, and technology enthusiasts who want to build eco-friendly AI systems. No advanced hardware or complex mathematical background is required to start. Begin your journey toward building efficient, sustainable technology today.