Model Optimization

AI Model Performance Enhancement for Production

Advanced techniques for model compression, acceleration, and performance optimization

🚀
10x

faster after optimization

📦
90%

model size reduction

75%

energy savings

Model Optimization Techniques

Q Quantization

Numerical Precision Reduction

Reduce data size from 32-bit to 16-bit, 8-bit or lower

FP32 → FP16: 2x faster
FP32 → INT8: 4x faster

Dynamic Quantization

Adjust precision dynamically based on data

Accuracy retention: 99.5%
Size reduction: 75%

P Model Pruning

Structured Pruning

Parameter reduction: 50-90%
Speed improvement: 3-5x

Unstructured Pruning

Flexibility: High
Performance retention: 95%

Magnitude-based Pruning

Simplicity: High
Effectiveness: Good

Knowledge Distillation

Knowledge Transfer Process

1

Teacher Model

Large, high-performance model with superior accuracy

2

Student Model

Smaller model learning from teacher's knowledge

3

Soft Targets

Use probability distributions instead of hard labels

Optimization Results

Model Size

Teacher: 500MB → Student: 50MB

Inference Speed

Teacher: 100ms → Student: 10ms

Accuracy

Teacher: 95.5% → Student: 94.2%

Ready to Optimize Your AI Models?

Consult our AI model optimization experts