When should I use pruning or quantization?

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

Pruning reduces the size of a model by removing less important weights, while quantization reduces the precision of weights.

When should I choose pruning over quantization?

Choose pruning when you want to maintain model accuracy while reducing size, especially for large models.

Are there scenarios where I should use both techniques?

Yes, using both can optimize model performance and efficiency, especially in resource-constrained environments.

What are the potential downsides of using these techniques?

Pruning can lead to loss of accuracy if not done carefully, and quantization may introduce quantization errors.