Master the theoretical foundations of Large Language Model (LLM) quantization and compression, and understand how state-of-the-art AI models are optimized for efficient deployment—without writing a single line of code.
Modern Large Language Models contain billions of parameters, making them computationally expensive to train and deploy. Building production-ready AI systems requires far more than programming skills; it demands a deep understanding of model optimization, mathematical principles, hardware constraints, compression techniques, and architectural trade-offs.
This course is designed to build those conceptual foundations from first principles. Rather than focusing on implementation details or coding syntax, you will develop the mental models necessary to understand how LLMs are compressed, accelerated, and deployed efficiently across cloud, edge, and mobile environments.
