Module 4: Model Quantization

Introduction

Having optimized your vLLM performance, it’s time to tackle the most impactful optimization technique: model quantization. This module teaches you to compress LLM weights and activations to dramatically reduce memory requirements and inference costs while preserving model quality.

Quantization is transformative because it addresses the fundamental challenge of modern LLMs: their massive size. By reducing numerical precision from 16-bit to 8-bit or 4-bit representations, you can achieve 50-75% memory reduction, enabling deployment on smaller hardware and significant cost savings.

Why quantization matters:

  • Cost Reduction: Deploy larger models on less expensive hardware, reducing infrastructure costs by 2-4X

  • Memory Efficiency: Fit models that previously required multiple GPUs onto single GPU deployments

  • Inference Speed: Reduced data movement and optimized compute paths can improve throughput

  • Democratization: Make state-of-the-art models accessible to organizations with limited GPU budgets

Learning Objectives

By the end of this module, you will be able to:

  • Understand quantization fundamentals: weights, activations, and precision trade-offs

  • Implement W4A16 and W8A8 quantization schemes using LLM Compressor

  • Apply advanced techniques like SmoothQuant and GPTQ for optimal accuracy preservation

  • Build automated quantization pipelines using OpenShift AI and evaluate compressed models

  • Make informed decisions about quantization schemes based on hardware and use case requirements

What You’ll Learn

Quantization Fundamentals

  • Precision Formats: Understanding FP16, INT8, INT4 and their memory/performance implications

  • Weight vs Activation Quantization: When and how to quantize different model components

  • Quantization Schemes: W4A16, W8A8, and selecting the right approach for your hardware

  • Quality vs Efficiency Trade-offs: Balancing compression ratio with model accuracy

Advanced Quantization Techniques

  • SmoothQuant: Smoothing activation outliers for better weight/activation quantization

  • GPTQ: Layer-wise quantization optimization for minimal accuracy loss

  • Calibration Datasets: Selecting representative data for optimal quantization parameters

  • Hardware Considerations: Matching quantization schemes to GPU capabilities (Ampere vs Hopper)

Production Implementation

  • LLM Compressor Workflows: Using the industry-leading quantization toolkit

  • Pipeline Automation: Building repeatable quantization workflows in OpenShift AI

  • Quality Evaluation: Measuring accuracy impact and performance improvements

  • Deployment Integration: Serving quantized models with vLLM for production workloads

Module Structure

4.1 Quantization Fundamentals

Deep dive into quantization theory, precision formats, and decision frameworks for selecting optimal quantization schemes based on your specific hardware and accuracy requirements.

4.2 Hands-on Quantization Lab

Practical implementation using LLM Compressor to quantize models with W4A16 techniques, including SmoothQuant and GPTQ optimization for maximum quality preservation.

4.3 Production Quantization Pipelines

Build automated, repeatable quantization workflows using OpenShift AI, evaluate results, and integrate quantized models into production vLLM deployments.

Prerequisites

Before starting this module, ensure you have:

  • Completed Modules 2-3 (Evaluation, Optimization)

  • Understanding of vLLM serving and performance concepts

  • Access to OpenShift AI environment with GPU resources

  • Familiarity with model evaluation and benchmarking from previous modules

Real-World Impact

Consider these quantization results from enterprise deployments:

  • Memory Reduction: 70B parameter models reduced from 140GB to 35GB (W4A16)

  • Cost Savings: 400B parameter model deployment cost reduced by 60% through quantization

  • Hardware Accessibility: Models requiring 8x A100 GPUs compressed to run on 2x A100 GPUs

  • Maintained Quality: <2% accuracy degradation with proper quantization techniques

Success Metrics

By module completion, you should achieve:

  • Successful Model Compression: Reduce model memory footprint by 50-75%

  • Quality Preservation: Maintain >95% of original model accuracy

  • Production Pipeline: Automated quantization workflow ready for enterprise deployment

  • Cost Analysis: Clear understanding of infrastructure savings and deployment options

  • Technical Confidence: Ability to recommend and implement quantization strategies for different use cases

What’s Next

This module bridges the gap between research and production, giving you the tools to deploy enterprise-grade compressed models that maintain quality while dramatically reducing costs.

Ready to unlock the full potential of LLM quantization? Let’s begin!