Compress LLMs Like a Pro: FP8, GPTQ & SmoothQuant Explained

🚀 Large Language Models are powerful, but they can be expensive to run. In this tutorial, you'll learn how to use llmcompressor to apply post-training quantization (PTQ) techniques that reduce model size, improve inference speed, and lower deployment costs without retraining your model. Using the Qwen2.5 model as a practical example, we'll explore multiple quantization strategies, benchmark their performance, and evaluate the trade-offs between efficiency and model quality. 📌 What You'll Learn ✅ What is Post-Training Quantization (PTQ)? ✅ Introduction to the llmcompressor library ✅ FP8 Dynamic Quantization explained ✅ GPTQ Quantization workflow ✅ SmoothQuant implementation and use cases ✅ Preparing and using calibration datasets ✅ Measuring model perplexity and output quality ✅ Benchmarking inference throughput and latency ✅ Comparing compression techniques side-by-side ✅ Optimizing Qwen2.5 for production deployment ✅ Best practices for efficient LLM serving