Real time rendering of en face optical coherence tomography (OCT) and OCT-angiography (OCTA) of arbitrary retinal layers in ophthalmic imaging sessions can be used to increase the yield rate of high-quality acquisitions, provide real-time feedback during image-guided surgeries and compensate aberrations in sensorless adaptive optics (AO) OCT and OCTA. However, real-time en face visualizations rely critically on the accurate segmentation of retinal layers in the three-dimensional OCT volumes. Here, we demonstrate a compact deep-learning architecture that segmented batches of OCT B-scans and produced the corresponding OCT and OCTA projections within only 41 ms. The short latency was possible due to a low complexity neural network structure, CNN compression using TensorRT, and the use of Tensor Cores on GPU hardware to accelerate the computation of convolutions. Inferencing of the original U-net was accelerated by 21 times without reducing the accuracy. To the best our knowledge, our work is the first demonstration of an ophthalmic imager with embedded artificial intelligence (AI) providing real-time feedback.