We research differentially personal stochastic convex optimization (DP-SCO) underneath user-level privateness the place every consumer might maintain a number of information objects. Present work for user-level DP-SCO both requires super-polynomial runtime or requires variety of customers that grows polynomially with the dimensionality of the issue. We develop new algorithms for user-level DP-SCO that receive optimum charges, run in polynomial time, and require a variety of customers that develop logarithmically within the dimension. Furthermore, our algorithms are the primary to acquire optimum charges for non-smooth features in polynomial time. These algorithms are primarily based on multiple-pass DP-SGD, mixed with a novel personal imply estimation process for concentrated information, which applies an outlier elimination step earlier than estimating the imply of the gradients.