LLM Fine-Tuning 13: LLM Quantization Explained (PART 2) | PTQ, QAT, GPTQ, AWQ, GGUF, GGML, llama.cpp

Welcome to Episode 13 of the LLM Fine-Tuning Series — Quantization Part 2! In this video, we move beyond the basics and explore advanced quantization techniques that power today’s large language models (LLMs). If you’ve watched Part 1 (Introduction to Quantization), you’re now ready to dive into real-world algorithms and formats like GPTQ, AWQ, GGML, GGUF, and QAT. What You’ll Learn in This Video 1️⃣ Advanced LLM Quantization Overview – Why scaling models require smarter compression 2️⃣ GPTQ (Gradient Post-Training Quantization) – How it reduces memory while preserving accuracy 3️⃣ AWQ (Activation-Aware Weight Quantization) – Minimizing quantization error with smarter calibration 4️⃣ Quantization-Aware Training (QAT) – Teaching models to learn with quantization noise 5️⃣ GGML (Georgi Gerganov’s Machine Learning Library) – Lightweight inference engine for CPUs & edge devices 6️⃣ GGUF (General Unified Format) – Standardized, future-proof quantization format for LLMs 7️⃣ Hands-on Practical Demo with LLMs – Applying quantization and measuring speed & memory gains 8️⃣ Choosing the Right Technique – When to use GPTQ, AWQ, GGUF, or QAT By the end of this video, you’ll be equipped to deploy massive LLMs on limited hardware with blazing speed and lower costs. Material & Resources: https://github.com/sunnysavita10/Complete-LLM-Finetuning/tree/main/LLM-Quantization-Part-2 🔔 Like, Share & Subscribe to stay updated with the full LLM fine-tuning playlist. Got questions or topic requests? Drop a comment below 👇. ⏱️ Timestamps / Chapters: 00:00 Introduction to LLM Quantization 06:13 Advanced LLM Quantization Concepts 27:19 PTQ vs QAT for Large Language Models 44:52 GPTQ Theory (Gradient Post-Training Quantization) 01:35:09 GPTQ Hands-on Practical Example 02:10:49 AWQ Theory (Activation-Aware Quantization) 02:27:07 AWQ Hands-on Practical Example 02:35:30 QAT (Quantization Aware Training) Theory + Practical 02:56:17 GGML & GGUF Explained for LLM Inference