Advances in Semi-Supervised Learning (SSL) have almost entirely closed the gap between SSL and Supervised Learning at a fraction of the number of labels. However, recent performance improvements have often come \textit{at the cost of significantly increased training computation}. To address this, we propose Curriculum Batch Size (CBS), \textit{an unlabeled batch size curriculum that exploits the natural training dynamics of deep neural networks.} A small unlabeled batch size is used at the beginning of training and gradually increases to the end. A fixed curriculum is used regardless of dataset, model, or number of epochs, and reduced training computations are demonstrated in all settings. We apply CBS, strong labeled augmentation, Curriculum Pseudo Labeling (CPL) \cite{FlexMatch} to FixMatch \cite{FixMatch} and term the new SSL algorithm Fast FixMatch. We perform an ablation study to show that strong labeled augmentation and CPL do not significantly reduce training computations, but, combined with CBS, they achieve appealing performance. Fast FixMatch also achieves substantially higher data utilization compared to the previous state-of-the-art. Fast FixMatch achieves between $2.1\times$ - $3.4\times$ reduced training computations on CIFAR-10 with all but 40, 250, and 4000 labels removed, compared to the original FixMatch, while attaining the same cited state-of-the-art error rate \cite{FixMatch}. Similar results are achieved for CIFAR-100, SVHN, and STL-10. Finally, Fast FixMatch achieves between $2.6\times$ - $3.3\times$ reduced training computations in federated SSL tasks and online/streaming learning SSL tasks and other SSL algorithms, which further demonstrate the generalizability of Fast FixMatch to different scenarios and tasks.