Practical Guide to AI Model Compression: Pruning, Quantization, and Knowledge Distillation

Why Model Compression is Necessary

Large AI models provide high accuracy but require substantial computational resources for inference. Compression is essential for running them on edge devices, mobile, and IoT devices.

Three Major Compression Techniques

1. Pruning

Reduces model size by removing neurons or connections with low importance.

2. Quantization

Converts FP32 (32-bit floating point) to INT8 (8-bit integer) to improve model size and inference speed.

3. Knowledge Distillation

Transfers knowledge from a large model (Teacher) to a smaller model (Student). The Student model retains over 90% of the Teacher's performance.

TensorRT Optimization

NVIDIA TensorRT optimizes models for GPU to maximize inference speed. It automatically applies quantization and layer fusion.

Conclusion

Model compression is an essential step for deploying AI in the field. POLYGLOTSOFT's AI platform provides automated compression pipelines.