Probabilistic computing with binary stochastic neurons (BSN) implemented with low- or zero-energy barrier nanoscale ferromagnets (LBMs) possessing in-plane magnetic anisotropy has emerged as an efficient paradigm for solving computationally hard problems. The fluctuating magnetization of an LBM at room temperature encodes a p-bit which is the building block of a BSN. Its only drawback is that the dynamics of common (transition metal) ferromagnets are relatively slow and hence the number of uncorrelated p-bits that can be generated per second – the so-called “flips per second” (fps) – is insufficient, leading to slow computational speed in autonomous co-processing with p-computers. Here, we show that a simple way to increase fps is to replace commonly used ferromagnets (e.g. Co, Fe, Ni), which have large saturation magnetization Ms, with a dilute magnetic semiconductor like GaMnAs with much smaller saturation magnetization. The smaller Ms reduces the energy barrier within the LBM and increases the fps significantly. It also offers other benefits such as increased packing density for increased parallelization and reduced device to device variation. This provides a way to realize the hardware acceleration and energy efficiency promise of p-computers.