17. Quantization

17. Quantization#

Reducing Model Size Without Losing Accuracy#

Quantization is a critical technique for deploying ML models in production, especially on edge devices. In this module, you’ll learn how to reduce model size and increase inference speed by converting floating-point weights to lower precision formats.

What You’ll Build#

  • INT8 Quantization: Convert 32-bit floats to 8-bit integers

  • Quantization-Aware Training: Train models that quantize well

  • Dynamic Quantization: Quantize activations at runtime

  • Static Quantization: Pre-compute quantization parameters

Why This Matters#

Modern ML models are often too large for deployment:

  • GPT models can be hundreds of gigabytes

  • Mobile devices have limited memory

  • Edge computing requires efficient models

  • Quantization can reduce model size by 75% with minimal accuracy loss

Learning Objectives#

By the end of this module, you will:

  • Understand the trade-offs between model size and accuracy

  • Implement INT8 quantization from scratch

  • Build quantization-aware training pipelines

  • Measure the impact on model performance

Prerequisites#

Before starting this module, you should have completed:

  • Module 02: Tensor (for basic operations)

  • Module 04: Layers (for model structure)

  • Module 08: Training (for fine-tuning quantized models)

Real-World Applications#

Quantization is used everywhere in production ML:

  • Mobile Apps: TensorFlow Lite uses INT8 for on-device inference

  • Edge Devices: Raspberry Pi and Arduino deployment

  • Cloud Inference: Reducing serving costs at scale

  • Neural Processors: Apple Neural Engine, Google Edge TPU

Coming Up Next#

After mastering quantization, you’ll explore:

  • Module 18: Compression - Further model size reduction techniques

  • Module 19: Caching - Optimizing inference latency

  • Module 20: Benchmarking - Measuring the impact of optimizations

This module is currently under development. The implementation will cover practical quantization techniques used in production ML systems.

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