Imagine querying complex knowledge graphs in real time—right from Python—with all the performance of a GPU supercomputer and none of the usual code headaches. This session reveals how NVIDIA cuGraph turbocharges single-hop, multi-hop, and traversal operations on giant knowledge graphs, cutting response times from seconds to milliseconds. We’ll break down how cuGraph’s GPU-accelerated algorithms work seamlessly with popular Python tools and how you can combine cuGraph with deep learning frameworks like PyTorch for ultra-scalable AI and retrieval-augmented generation (RAG) pipelines. Join us for practical demos, hands-on advice, and approachable insights—whether you’re building enterprise reasoning engines, interactive agents, or next-gen graph-powered search. Unlock the full speed of your data, from Python, with just a few lines of code!