As datasets continue to grow in both size and complexity, CPU-based visualization pipelines often become bottlenecks, slowing down exploratory data analysis and interactive dashboards. In this session, we’ll demonstrate how GPU acceleration can transform Python-based interactive visualization workflows, delivering speedups of up to 50x with minimal code changes. Using libraries such as hvPlot, Datashader, cuxfilter, and Plotly Dash, we’ll walk through real-world examples of visualizing both tabular and unstructured data and demonstrate how RAPIDS, a suite of open-source GPU-accelerated data science libraries from NVIDIA, accelerates these workflows. Attendees will learn best practices for accelerating preprocessing, building scalable dashboards, and profiling pipelines to identify and resolve bottlenecks. Whether you are an experienced data scientist or developer, you’ll leave with practical techniques to instantly scale your interactive visualization workflows on GPUs.