PHWSODownload
Established
LLM Quantization and Compression Theoretical Core
Download this premium online course and learn through high-quality video training, practical lessons, and real-world demonstrations. Designed for beginners and experienced learners alike, the course provides a structured learning path that helps build professional skills with step-by-step instruction and hands-on examples. Perfect for self-paced learning, career development, and expanding technical or business knowledge, this comprehensive eLearning resource delivers valuable insights that can be applied immediately in real-world projects and professional environments.
Published 6/2026
Created by Bhushan S
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 49 Lectures ( 4h 2m ) | Size: 3.1 GB
Study how multi-billion parameter networks are compressed into low-precision representations for resource-constr...
What you'll learn
Master the core principles of Post-Training Quantization (PTQ). Deconstruct the architecture and tradeoffs of Activation-aware Weight Quantization (AWQ). Analyze the design patterns governing Low-Rank Adaptation (LoRA). Build a deep mental model of Pruning Theory at scale.Requirements
No coding experience is required. We focus entirely on system design and core theoretical concepts. A basic interest in technology systems, algorithms, or computer science architecture. No special software or local development environment setup is needed.Description
This course contains the use of artificial intelligence.
LLM Quantization & Compression: Theoretical Foundations (Programming-Free)
Master the theoretical foundations of Large Language Model (LLM) quantization and compression, and understand how state-of-the-art AI models are optimized for efficient deployment-without writing a single line of code.
Modern Large Language Models contain billions of parameters, making them computationally expensive to train and deploy. Building production-ready AI systems requires far more than programming skills; it demands a deep understanding of model optimization, mathematical principles, hardware constraints, compression techniques, and architectural trade-offs.
This course is designed to build those conceptual foundations from first principles. Rather than focusing on implementation details or coding syntax, you will develop the mental models necessary to understand how LLMs are compressed, accelerated, and deployed efficiently across cloud, edge, and mobile environments.
What You Will Learn
By the end of this course, you will understand
Mathematical foundations of model compression Post-Training Quantization (PTQ) Quantization-Aware Training (QAT) Activation-Aware Weight Quantization (AWQ) GPTQ and advanced quantization techniques Low-Rank Adaptation (LoRA) QLoRA and parameter-efficient fine-tuning Structured and unstructured pruning methods Knowledge Distillation Mixed-Precision Inference Hardware-aware optimization Performance, latency, memory, and scalability trade-offs Deployment strategies and production best practicesCourse Curriculum
Module 1 - Mathematical Foundations
Linear Algebra Matrix Factorization Numerical Optimization Probability Theory Information TheoryModule 2 - Foundations of Model Compression
Why Compression Matters Computational Complexity Memory Hierarchies Compression Taxonomy AI Deployment ChallengesModule 3 - Quantization Theory
Floating-Point Representation Integer Quantization Fixed-Point Arithmetic Dynamic vs. Static Quantization Quantization Error AnalysisModule 4 - Post-Training Quantization
PTQ Fundamentals Calibration Techniques Weight Quantization Activation Quantization Inference OptimizationModule 5 - Advanced Quantization
Activation-Aware Weight Quantization (AWQ) GPTQ SmoothQuant Mixed Precision Low-Bit QuantizationModule 6 - Parameter-Efficient Fine-Tuning
Low-Rank Adaptation (LoRA) QLoRA Adapter Architectures Matrix Decomposition Efficient Fine-Tuning StrategiesModule 7 - Pruning Theory
Structured Pruning Unstructured Pruning Sparse Neural Networks Magnitude-Based Pruning Lottery Ticket HypothesisModule 8 - Knowledge Distillation
Teacher-Student Architectures Distillation Loss Functions Feature Distillation Response Distillation Model Compression PipelinesModule 9 - Hardware-Aware Optimization
GPU Optimization TPU and Accelerator Architectures Edge AI Deployment Memory Bandwidth Optimization Compute EfficiencyModule 10 - Architectural Trade-offs
Accuracy vs. Compression Latency vs. Throughput Memory vs. Compute Cost vs. Performance Scalability vs. Model SizeModule 11 - Responsible AI & Governance
Explainable AI Model Evaluation Benchmarking Ethical AI Deployment Governance FrameworksModule 12 - Production LLM Systems
Enterprise Deployment Architectures Inference Pipelines Serving Infrastructure Monitoring & Observability Future Directions in Efficient LLMsWho this course is for
Hardware-Software Co-designers, AI Platform Architects, SREsHomepage
Code:
https://www.udemy.com/course/llm-quantization-and-compression-theoretical-coreDOWNLOAD LINKS