LLM Fine-Tuning 12: LLM Quantization Explained( PART 1) | PTQ, QAT, GPTQ, AWQ, GGUF, GGML, llama.cpp

Welcome to Episode 12 of the LLM Fine-Tuning Series — In this Part 1 of our Quantization journey, we dive deep into the foundational concepts behind compressing and accelerating Large Language Models (LLMs) using Quantization. In this video, you'll learn: 1️⃣ What is Quantization in Deep Learning? 2️⃣ Why Quantization is Critical for Efficient Inference 3️⃣ Difference between RAM and VRAM (with real-world examples) 4️⃣ Data Types (int8, float16, etc.) & Their Memory Footprint 5️⃣ What is Precision in Deep Learning Models 6️⃣ Core Quantization Formula:   • Symmetric vs. Asymmetric   • Per-Tensor vs. Per-Channel 7️⃣ Types of Quantization:   • PTQ: Post-Training Quantization     ▪ Static PTQ     ▪ Dynamic PTQ   • QAT: Quantization-Aware Training 8️⃣ Understanding Calibration Data & Quantization Error 9️⃣ Practical Hands-on Example: Neural Network Quantization Demo This video sets the foundation for upcoming parts where we’ll cover real tools like GPTQ, AWQ, GGUF, GGML, and llama.cpp in detail. Don’t miss it! 👉 If you're an ML Engineer, Researcher, or someone building on LLMs — this series will level up your deployment game. Stay tuned for **Part 2**: GPTQ, AWQ, GGUF, and more — coming soon! LLM Fine-Tuning Material: https://github.com/sunnysavita10/Complete-LLM-Finetuning/tree/main/LLM_Quantization 🔔 Like, Share & Subscribe to stay updated with the full LLM fine-tuning playlist. Got questions or topic requests? Drop a comment below 👇. 📌 Keywords Covered: #LLMFineTuning #LLMQuantization #GPTQ #PTQ #QAT #AWQ #GGUF #GGML #llamaCpp #DeepLearning #NeuralNetworkOptimization #Transformers #HuggingFace #LangChain #LangGraph #RAG #AdvancedRAG #AIAgents #AgenticAI #GenerativeAI #LLMTutorial #AIProjects #AIForDevelopers #TransferLearning #FineTuning #PretrainedModels #OpenSourceAI #LLM #MachineLearning #ArtificialIntelligence #AITutorial #Python #Chatbot #StructuredOutput #PromptEngineering #TextGeneration #Embedding #LLMWorkflow #S