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In this poster, we will show how to leverage NVidia’s Bluef ield Data Processing Unit (DPU) in geospatial systems. Existing work in literature has explored DPUs in the context of machine learning, compression and MPI acceleration. We show our designs on how to integrate DPUs into existing high performance geospatial systems like MPI-GIS. The workflow of a typical spatial computing workload consists of two phases- filter and refine. First we used DPU as a target to offload spatial computations from the host CPU. We show the performance improvements due to offload. Next we used DPU for network I/O processing. In network I/O case, the query data first comes to DPU for filtering and then the query goes to CPU for refinement. DPU-based filter and refine system can be useful in other domains like Physics where an FPGA is used to perform the filter to handle Big Data. We used Bluefield-2 and Bluefield-3 in our experiments. For scalability study, we have used up to 16 DPUs.