In this paper, we present an optimized GPU based implementation of Probabilistic Latent Sequential motifs (PLSM) that was proposed for sequential pattern mining from video sequences. PLSM mines for recurrent sequential patterns from documents given as word-time occurrences, and outputs a set of sequential activity motifs and their starting occurrences. PLSM’s uniqueness comes from modeling the co-occurrence and temporal order in which the words occur within a temporal window while also dealing with activities which occur concurrently in the video. However, the expectation-maximization algorithm used in PLSM has a very high time complexity due to complex nested loops, requiring several dimensionality reduction steps before invoking PLSM. In order to truly realize the benefits of the model, we propose two GPU based implementations of PLSM called GPU-pLSM (sparse and dense). The two implementations differ based on whether the entire word-count matrix (dense) or only the non-zero entries (sparse) are considered in inferring the latent motifs respectively. Our implementation achieves an impressive 265X and 366X times speed up for dense and sparse approaches respectively on NVIDIA GeForce GTX Titan. This speed up enables us to remove several pre-processing and dimension reduction steps used to generate the input temporal documents and thus apply PLSM directly on the input documents. We validate our results through qualitative comparisons of the inferred motifs on two different publicly available datasets. Quantitative comparison on document reconstruction based abnormality measure show that both GPU-PLSM and PLSA+PLSM are strongly correlated.