Advanced Quantization Techniques for Large Language Models
49mAdvanced2026-01-15
Authors
Nayan Saxena
Course details
What is this course about?
Discover cutting-edge quantization techniques for large language models, focusing on the algorithms and optimization strategies that deliver the best performance. Instructor Nayan Saxena begins by covering mathematical foundations, before progressing through advanced methods including GPTQ, AWQ, and SmoothQuant with hands-on examples in Google Colab. Along the way, gather quick tips to master critical concepts such as precision formats, calibration strategies, and evaluation methodologies. Leveraging both theoretical principles and practical applications, this course equips you with in-demand skills to significantly reduce model size and accelerate inference while maintaining performance quality.
Objectives
What will I be able to do by the end of this course?
Analyze the mathematical foundations of quantization and their impact on transformer architectures.
Apply state-of-the-art quantization techniques including GPTQ, AWQ, and SmoothQuant to LLMs.
Evaluate the trade-offs between different quantization approaches using appropriate metrics.
Optimize quantization results through advanced calibration strategies.
Compare and select quantization methods based on model architecture and use case requirements.
Audience
Who is this course for?
Machine learning engineers
AI practitioners
Technical leads working with LLMs
Discover cutting-edge quantization techniques for large language models, focusing on the algorithms and optimization strategies that deliver the best performance. Instructor Nayan Saxena begins by covering mathematical foundations, before progressing through advanced methods including GPTQ, AWQ, and SmoothQuant with hands-on examples in Google Colab. Along the way, gather quick tips to master critical concepts such as precision formats, calibration strategies, and evaluation methodologies. Leveraging both theoretical principles and practical applications, this course equips you with in-demand skills to significantly reduce model size and accelerate inference while maintaining performance quality.
Objectives
What will I be able to do by the end of this course?
Analyze the mathematical foundations of quantization and their impact on transformer architectures.
Apply state-of-the-art quantization techniques including GPTQ, AWQ, and SmoothQuant to LLMs.
Evaluate the trade-offs between different quantization approaches using appropriate metrics.
Optimize quantization results through advanced calibration strategies.
Compare and select quantization methods based on model architecture and use case requirements.
Audience
Who is this course for?
Machine learning engineers
AI practitioners
Technical leads working with LLMs
Concepts
Introduction
- Quantization in modern LLMs
Mathematical Foundations
- Introduction to quantization and number precision formats
- Quantization error analysis
Post-Training Quantization
- Uniform vs. non-uniform quantization schemes
- Quantizing your first Transformer model
State-of-the-Art Quantization Algorithms
- GPTQ - Principles and practical application
- AWQ - Principles and practical application
- SmoothQuant and emerging techniques
Quantization-Aware Training and Hardware Optimization
- QAT fundamentals for Transformers
- Hardware-specific optimization strategies
Conclusion
- Future directions in LLM quantization