LLM Inference Optimization

This course provides a comprehensive overview of techniques to enhance the performance of large language models (LLMs) during inference. It begins with an introduction to the principles of LLM inference optimization, focusing on the transformer architecture and various optimization strategies. Participants will explore advanced methods, including quantization and speculative decoding, to reduce model complexity and improve execution speed. The course also covers model parallelism and sharding techniques for effective deployment in real-world applications. Finally, learners will complete a project on accelerating news headline generation using LLM optimization, demonstrating practical implementations of the concepts discussed.