<p>In this work, the fast-convolving reproducing kernel particle method (FC-RKPM) is introduced. This method is hundreds to millions of times faster than the traditional RKPM for 3D meshfree simulations. In this approach, the meshfree discretizations with RK approximation are expressed in terms of convolution sums. Fast Fourier transform is then used to efficiently compute the convolutions. Certain modifications to the domain and shape functions are considered to maintain generality for complex geometries and arbitrary boundary conditions. The new method does not need to identify, store, and loop over the neighbors, and therefore, bypasses this bottleneck in the traditional approach. As a result, the run-times and memory allocations are independent of the number of neighbors and the shape function’s support size. As a model problem, the method is laid out for a Galerkin weak form of the Poisson problem with the RK approximation, and is verified in 1D, 2D, and 3D. Tables with run-times and allocated memory are presented to compare the performance of FC-RKPM with the traditional method in 3D. The performance is studied for various node numbers, support size, and approximation degree. All the implementation details and the roadmap for software development are also provided. Application of the new method to nonlinear and explicit problems are briefly discussed as well.</p>