The L1-regularized Gaussian maximum likelihood method is a common approach for sparse precision matrix estimation but one that poses a computational challenge for high-dimensional datasets. I will present a novel method for performant large-scale sparse precision matrix estimation utilizing the block structures and parallelism in the underlying computations for added performance and scalability. Our numerical examples and comparative results with various modern open-source packages reveal that the proposed algorithm provides orders of magnitude faster runtimes and scales to significantly higher-dimensional datasets.