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At short exposures, however, pixels record only a few discrete photon counts, and this quantization introduces both statistical and computational challenges. We present QTrack, a likelihood-based and parallelized framework that directly models discrete photon detections while exploiting correlations across all frames. By doing so, QTrack surpasses the Cram\u00e9r \u2013 Rao lower bound (CRLB) prediction assuming continuous intensity and localization-and-linking. To make this possible in practice, QTrack exploits the parallelism inherent in both likelihood evaluation and posterior sampling through vectorization, multi-threading, and GPU acceleration, with lightweight interthread communication. On a single mid-range GPU (Nvidia GTX 1060, 6 GB), QTrack achieves up to a 50-fold speedup compared to a serial CPU implementation, while maintaining full accuracy and data efficiency. 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