Current clinical 3D-DSA requires the acquisition of two image volumes, before and after the injection of contrast media (i.e. mask and fill scans). Deep learning angiography (DLA) is a recently developed technique that enables the generation of mask-free 3D angiography using convolutional neural networks (CNN). In this work, the quantitative performance of DLA as a function of the number of layers in the deep neural network and the DLA inference computation time are investigated. Clinically indicated rotational angiography exams of 105 patients scanned with a C-arm conebeam CT system using a standard 3D-DSA imaging protocol for the assessment of cerebrovascular abnormalities were retrospectively collected. More than 185 million labeled voxels from contrast-enhanced images of 43 subjects were used as training and testing dataset. Multiple deep CNNs were trained to perform DLA. The trained DLA models were then applied in a validation cohort consisting of the remaining image volumes from 62 subjects and accuracy, sensitivity, precision and F1-scores were calculated for vasculature classification in relevant anatomy. The implementation of the best performing model was optimized for accelerated DLA inference and the computation time was measured under multiple hardware configurations. <p> </p>Vasculature classification accuracy and 95% CI in the validation dataset were 98.7% ([98.3, 99.1] %) for the best performing model. DLA inference user time was 17 seconds for a throughput of 23 images/s. In conclusion, a 30-layer DLA model outperformed shallower networks and DLA inference computation time was demonstrated not be a limiting factor for current clinical practice.