Jensen Paris june 2025 https://www.youtube.com/watch?v=X9cHONwKkn4 Each market, each domain of application. Becomes accelerated. Each one of these 5:52 libraries opens up new opportunities for the developers and it opens up new opportunities 5:59 for growth for us and our ecosystem partners. Computational lithography. Probably the single 6:06 most important application in semiconductor design today, runs in a factory at TSMC. 6:12 Samsung large semiconductor fabs. Before the chip is made, it runs through an inverse physics 6:20 algorithm called cuLitho, computational lithography. 6:25 Direct sparse solvers Algebraic multi-grid solvers. 6:31 CuOpt, we just opened sourced. incredibly exciting Application Library. 6:38 This library accelerates decision making To optimize problems with millions of 6:46 variables for millions of constraints like traveling salespeople problems. 6:51 Warp, a Pythonic Framework for expressing 6:56 geometry and physics solvers…really important. cuDF cuML, Structured databases, DataFrames, 7:07 classical machine learning algorithms cuDf accelerates Spark with zero 7:13 lines of code change. cuML accelerates Sidekick learn with zero lines of code change. Dynamo and CuDNN 7:22 cuDNN is probably the single most important library NVIDIA has ever created. It accelerates 7:29 the primitives of deep neural networks. And Dynamo is our brand new library that makes 7:35 it possible to dispatch, orchestrate, distribute extremely complex inference 7:42 workloads across an entire AI factory. cuEquivariance and cuTensor tensor 7:48 contraction algorithms. Equivariance is for neural networks that obey the laws of geometry, such as proteins, molecules, Aerial, and Sionna. Really 8:00 important framework to enable AI to run 6G. Earth-2, our simulation environment for 8:11 foundation models of weather and climate models. Kilometer-squared, incredibly high resolution. 8:19 MONAI, our framework for medical imaging, incredibly popular. Parabricks is a solver 8:26 for genomics analysis incredibly successful. cuQuantum, CUDA-Q I'll talk about in just 8:33 a second for quantum computing and cuPyNumeric acceleration for NumPy and 8:40 SciPy. As you can see, these are just a few of the examples of libraries. There are 400 others. 8:48 Each one of them accelerates a domain of application. Each one of them opens up new opportunities. Well, one of the most exciting. 8:58 Most exciting is CUDA-Q. CUDA-X is this suite of libraries. 9:05 Library suite for accelerating applications and algorithms on top of CUDA. We now have CUDA-Q. 9:12 CUDA-Q is for quantum computing. For quantum classical computing based on GPUs. CUDA-Q 9:23 We've been working on CUDA now for several years. And today I can tell you there's an inflection 9:30 point happening in quantum computing.