Single molecule localization microscopy (SMLM) has revolutionized the understanding of cellular organization by reconstructing informative images with quantifiable spatial distributions of molecules far beyond the optical diffraction limit. Much effort has been devoted to optimizing localization accuracy. Among them, assessing the quality of SMLM data in real-time, rather than after lengthy post-acquisition analysis, represent a computational challenge. Here, we overcome this difficulty by implementing an innovative mathematical approach to drastically reduce the computational analysis of particle localization. We have therefore designed the Quality Control Map (QCM) workflow to process data at a much higher rate than that limited by the frequency required by current cameras. Moreover, QCM requires no parameters other than the PSF radius characteristic of the optical system and only a GPU card to reach its computational speed. Thus, QCM is robust and adaptable to any type of input data. Finally, the QCM off-line mode can be used to evaluate synthetic or previously acquired data, and as a tool for teaching the basic concepts of the SMLM approach. Teaser QCM, a parameter-free algorithm, calculates indicators for instant feedback on single-molecule localization precision experiments