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V100, P40, and T4 are examples which are all classified as performance optimized graphics processors. NVIDIA GPUs are classified by either performance, density, or blade optimized. This advancement allowed ArcGIS Pro to leverage the GPU for compute as well as Imagery. Advancements in the Pascal architecture and later, allow for both visualization as well as high performance computing simultaneously. Since then, the Maxwell, Pascal, Volta, Turing, and the recently announced Ampere architecture. The first being the NVIDIA GRID Kepler architecture series, which was released in 2012. NVIDIA has produced many GPUs over the recent years. Heavy, High-end cartography and extensive analysis.Medium, Advanced data management and analysis, Deep Learning Inferencing.ArcGIS Pro user types could be broken down in three categories: The GPU can also be used for inference when configured to use TensorFlow. Tools such as Slope, Aspect, Viewshed2 and others take advantage of the GPU and offset the CPU usage. New tools continue to be introduced that can be used for GPU compute.
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Geoprocessing as well can make use of the GPU.
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Additional profiles offer more frame buffer from the GPU which can positively affect the user experience. As workload demands on the GPU increase, as well as the additional needs from the operating system, additional frame buffer can be needed. A 1Q or 1 GB frame buffer profile allows for the overall needed functionality. To get the best ArcGIS Pro user experience the minimum recommended frame buffer is 1024 MB (1 GB), which is shown as the 1Q profile. To achieve the best return on investment, density, or functionality, profiles are assigned to each VM. This is where the GPU profiles come into play. Making the best use of a resource such as a GPU involves scalability when sharing GPU functionality. Running ArcGIS Pro requires the use of a GPU.