I was reading up the difference between parametric and non-parametric models on this site: https://sebastianraschka.com/faq/docs/parametric_vs_nonparametric.html
It says that linear SVM is parametric but RBF is non-parametric because "In the RBF kernel SVM, we construct the kernel matrix by computing the pair-wise distances between the training points, which makes it non-parametric."
The linear SVM uses the primal form which is :
whereas kernalized SVMs use only the dual form which is:
From the above equations, I don't see how linear SVM are parametric but Kernalized ones are non- parametric.