Model Quantization Techniques for Efficient Inference
Model Quantization Techniques for Efficient Inference
Quantization reduces the precision of model weights to make inference faster and more memory-efficient.
1) What is Quantization?
Convert 32-bit floating point weights to 16-bit or 8-bit representations.
2) Benefits
- Lower memory footprint
- Faster inference
- Reduced GPU cost
3) Trade-Offs
Slight reduction in accuracy may occur. Careful evaluation is required.
4) Enterprise Application
Quantization is widely used in edge deployment and large-scale serving.
5) Summary
Quantization makes large models practical in production environments.
