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Augmented Reality (AR) is an application of computer vision that is processor intensive and typically suffers from a trade-off between robust view alignment and real time performance. Real time AR that can function robustly in variable environments is a process difficult to achieve on a PC (personal computer) let alone on the mobile devices that will likely be where AR is adopted as a consumer application. Despite the availability of high quality feature matching algorithms such as SIFT, SURF and robust pose estimation algorithms such as EPNP, practical AR systems today rely on older methods such as Harris/KLT corners and template matching for performance reasons. SIFT-like algorithms are typically used only to initialize tracking by these methods. We demonstrate a practical system with real ime performance using only SURF without the need for tracking. We achieve this with extensive use of the Graphics Processing Unit (GPU) now prevalent in PC's. Due to mobile devices becoming equipped with GPU's we believe that this architecture will lead to practical robust AR.