LLM Quantization and Compression Theoretical Core

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 practices
Course Curriculum
Module 1 – Mathematical Foundations
✨ Linear Algebra
✨ Matrix Factorization
✨ Numerical Optimization
✨ Probability Theory
✨ Information Theory
Module 2 – Foundations of Model Compression
✨ Why Compression Matters
✨ Computational Complexity
✨ Memory Hierarchies
✨ Compression Taxonomy
✨ AI Deployment Challenges
Module 3 – Quantization Theory
✨ Floating-Point Representation
✨ Integer Quantization
✨ Fixed-Point Arithmetic
✨ Dynamic vs. Static Quantization
✨ Quantization Error Analysis
Module 4 – Post-Training Quantization
✨ PTQ Fundamentals
✨ Calibration Techniques
✨ Weight Quantization
✨ Activation Quantization
✨ Inference Optimization
Module 5 – Advanced Quantization
✨ Activation-Aware Weight Quantization (AWQ)
✨ GPTQ
✨ SmoothQuant
✨ Mixed Precision
✨ Low-Bit Quantization
Module 6 – Parameter-Efficient Fine-Tuning
✨ Low-Rank Adaptation (LoRA)
✨ QLoRA
✨ Adapter Architectures
✨ Matrix Decomposition
✨ Efficient Fine-Tuning Strategies
Module 7 – Pruning Theory
✨ Structured Pruning
✨ Unstructured Pruning
✨ Sparse Neural Networks
✨ Magnitude-Based Pruning
✨ Lottery Ticket Hypothesis
Module 8 – Knowledge Distillation
✨ Teacher-Student Architectures
✨ Distillation Loss Functions
✨ Feature Distillation
✨ Response Distillation
✨ Model Compression Pipelines
Module 9 – Hardware-Aware Optimization
✨ GPU Optimization
✨ TPU and Accelerator Architectures
✨ Edge AI Deployment
✨ Memory Bandwidth Optimization
✨ Compute Efficiency
Module 10 – Architectural Trade-offs
✨ Accuracy vs. Compression
✨ Latency vs. Throughput
✨ Memory vs. Compute
✨ Cost vs. Performance
✨ Scalability vs. Model Size
Module 11 – Responsible AI & Governance
✨ Explainable AI
✨ Model Evaluation
✨ Benchmarking
✨ Ethical AI Deployment
✨ Governance Frameworks
Module 12 – Production LLM Systems
✨ Enterprise Deployment Architectures
✨ Inference Pipelines
✨ Serving Infrastructure
✨ Monitoring & Observability
✨ Future Directions in Efficient LLMs

Who this course is for

⭐ Hardware-Software Co-designers, AI Platform Architects, SREs

Homepage

https://www.udemy.com/course/llm-quantization-and-compression-theoretical-core

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