Model Compression reduces the size, memory usage, and computational requirements of neural networks while maintaining acceptable performance for deployment in resource-constrained environments.
Compression Goals:
• Size Reduction: Smaller file sizes for storage/transfer
• Speed: Faster inference for real-time applications
• Memory: Lower RAM requirements for mobile devices
• Energy: Reduced power consumption for edge computing
💡 The Mobile Reality
Modern smartphones have ~6GB RAM, but a large language model can be 175GB! Compression makes AI accessible everywhere.
Compression Techniques
🎓
Knowledge Distillation
Large teacher model trains smaller student model
✂️
Network Pruning
Remove unnecessary weights and connections
📏
Quantization
Reduce precision of weights and activations
🔢
Low-Rank Factorization
Decompose weight matrices into smaller factors
Model Compression Impact
Original Model
Size:500 MB
Params:100M
Inference:100ms
Accuracy:95%
→
Compressed Model
Size:50 MB (10x smaller)
Params:10M (10x fewer)
Inference:20ms (5x faster)
Accuracy:93% (minimal loss)
Result: 10x smaller model with only 2% accuracy drop - perfect for mobile deployment!