We'll present a GPU-accelerated deep-learning framework for cyber-manufacturing, which enables real-time feedback to designers regarding the manufacturability of a computer-aided design model. We'll talk about a 3D-convolutional neural network-based approach for learning the manufacturability of a mechanical component. The 3D-CNN can recognize the features in a CAD model and classify it to be manufacturable or non-manufacturable with a greater accuracy than traditional rule-based methods. We'll discuss a novel GPU-accelerated voxelization algorithm used to discretize the CAD model and prepare it for deep learning. We'll briefly outline the challenges in training a 3D-CNN using complex CAD models on a GPU (NVIDIA TITAN X) with limited memory. Finally, we'll touch upon different methods to extend the framework to other manufacturing processes, such as additive manufacturing and milling.