What is the intuition behind margin geometry in RKHS space?

In RKHS, SVM maximizes margin in a transformed infinite-dimensional space.

Kernel functions define geometry in RKHS where SVM finds hyperplane maximizing separation. Even though space may be infinite-dimensional, optimization remains finite due to representer theorem.