What is the difference between pruning and quantization in AI models?

What is the difference between pruning and quantization in AI models?

Pruning involves removing unnecessary weights from a model to reduce its size, while quantization reduces the precision of weights.

How do these techniques affect model performance?

Pruning can lead to faster inference times and lower memory usage, but may slightly reduce accuracy. Quantization can also speed up inference and reduce memory, often with minimal impact on performance.

Are there specific scenarios where one is preferred over the other?

Yes, pruning is often preferred when you need to reduce model size significantly, while quantization is ideal for deploying models on hardware with limited precision.

Can I use both techniques together?

Absolutely! Combining pruning and quantization can maximize efficiency and performance, especially in resource-constrained environments.