Practical GenAI Model Quantization with Python

Running state-of-the-art generative AI models requires massive computational resources, making deployment expensive and slow. Quantization solves this by compressing models with minimal loss in accuracy, allowing them to run efficiently on standard hardware. In this course, you will transition from understanding basic model structures to actively compressing generative AI models using Python. You will learn how to reduce memory footprints and accelerate inference speeds, making your AI applications more practical, cost-effective, and ready for production deployment. What you'll learn: - Understand the fundamental math and concepts behind model quantization, including precision formats like FP16, INT8, and INT4. - Apply Post-Training Quantization (PTQ) to compress large language models using open-source Python libraries. - Configure modern quantization techniques such as GPTQ, AWQ, and GGUF for efficient local and cloud deployment. - Implement 8-bit and 4-bit precision loading to run large models on limited hardware. - Evaluate the performance, memory usage, and perplexity of compressed models to ensure generation quality remains high. Your learning journey begins with foundational concepts of neural network weights and precision before moving into hands-on Python compression workflows. You will read through step-by-step explanations, analyze optimized code blocks, and practice applying quantization strategies to real-world models. This course is designed for Python developers, aspiring AI engineers, and data scientists who want to optimize AI models. A basic understanding of Python and machine learning concepts is recommended, but no prior experience with model optimization or quantization is required. Start optimizing your generative AI models today and build faster, lighter applications.