🔢 Quantization Techniques
Reduce model size by 4x and accelerate inference with minimal accuracy loss
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Edge AI Deployment
Introduction to Quantization
🎯 What is Quantization?
Quantization reduces the precision of model weights and activations from 32-bit floats to lower bit representations (16-bit, 8-bit, or even 4-bit). This dramatically reduces model size and inference speed with minimal accuracy impact.
💡
Key Insight
Deep learning models are often over-parameterized and can tolerate lower precision.
📊 Precision Comparison
FP32 (32-bit Float)
BaselineSize
100 MB
Speed
1x
Accuracy
100%
FP16 (16-bit Float)
RecommendedSize
50 MB (2x)
Speed
1.8x
Accuracy
~99.9%
INT8 (8-bit Integer)
Best ratioSize
25 MB (4x)
Speed
3-4x
Accuracy
~99%
INT4 (4-bit Integer)
ExtremeSize
12.5 MB (8x)
Speed
5-6x
Accuracy
~95-98%
✨ Why Quantize?
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Edge Deployment
Fit models on mobile devices and embedded systems with limited memory
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Faster Inference
Integer operations are faster than floating-point on most hardware
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Cost Reduction
Serve more requests per GPU, reducing infrastructure costs
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Energy Efficiency
Lower precision consumes less power, extending battery life
🎯 When to Use
✅
Ideal Scenarios
- • Mobile and edge deployment
- • Production inference at scale
- • Real-time applications
- • Cost-sensitive deployments
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Considerations
- • Training still uses FP32/FP16
- • Some accuracy degradation
- • Hardware support needed for speedup