Optimizing the misclassification risk is in general NP-hard. Tractable solvers can be obtained by considering a surrogate regression problem. While convergence to the regression function is typically sublinear, the corresponding classification error can decay much faster. Fast and super fast rates (up to exponential) have been established for general smooth losses on problems where a hard margin is present between classes. This leaves out models based on non-smooth losses such as support vector machines, and problems where there is no hard margin, begging several questions. Are such models incapable of fast convergence? Are they therefore structurally inferior? Is the hard margin condition really necessary to obtain exponential convergence? Developing a new strategy, we provide an answer to these questions. In particular, we show not only that support vector machines can indeed converge exponentially fast, but also that they can do so even without hard margin.