Efficiency
Model Compression
41 SLIDES Part 4: Applications
The 350GB Problem: GPT-3 requires 350GB of memory. Your laptop has 16GB. That's 22x larger than your RAM. How do we make AI run anywhere?
Prerequisites
- Understanding of neural network architectures
- Week 6-7: Large language model basics
- Familiarity with floating-point number representation
Overview
Make models smaller and faster. Quantization, pruning, and knowledge distillation.
Learning Objectives
- Explain why model compression is essential for deployment
- Apply quantization techniques (INT8, INT4) to reduce model size
- Understand knowledge distillation for creating smaller models
- Compare pruning strategies (unstructured vs structured)
- Evaluate trade-offs between size, speed, and accuracy
Key Topics
Quantization
Pruning
Distillation
Inference optimization
Key Concepts
QuantizationReduce precision (FP32 to INT8/INT4) for 4-8x compression
Knowledge distillationTrain small "student" from large "teacher"
PruningRemove unnecessary weights/neurons
Model compression pipelineCombine techniques for maximum efficiency
Inference optimizationKV-cache, batching, hardware acceleration
Edge deploymentRunning models on phones and laptops
Key Visualizations
Quantization Levels
Distillation Architecture
Deployment Inference Pipeline
Efficiency