CUDA is NVIDIA’s platform for accelerated computing and the foundation for GPU computing. Programming Guide Tutorials and Samples Get Started With CUDA The NVIDIA® CUDA® Toolkit provides the development environment for creating high-performance, GPU-accelerated applications. The toolkit includes GPU-accelerated libraries, debugging and optimization tools, a C++ compiler, and a runtime library. As one of the most popular programming languages today for AI and high-performance computing (HPC), Python developers can build robust GPU applications directly in Python. NVIDIA CUDA Tile is the GPU programming model that simplifies the creation of optimized, tile-based kernels and targets portability for special-purpose hardware including Tensor Cores. Nsight Developer Tools NVIDIA Nsight™ tools are a powerful set of libraries, SDKs, and developer tools spanning across desktop and mobile targets. They enable developers to build, debug, profile, and develop software that utilizes the latest accelerated computing hardware. CUDA-X Libraries NVIDIA CUDA-X™, built on CUDA, is a collection of libraries that deliver dramatically higher performance across application domains, including AI and HPC. CUDA Fundamentals CUDA is NVIDIA's platform for accelerated computing, providing the software layer that enables applications to harness the power of GPUs. Developers can program in languages such as C++, Python, and Fortran or leverage GPU-accelerated libraries and frameworks like PyTorch. This flexibility lets developers integrate GPU computing into any layer of their software stack to achieve optimal functionality and performance. The CUDA Toolkit , an integral component of the CUDA platform, provides the compiler, libraries, and developer tools required to develop GPU applications. What’s CUDA All About Anyway? Learn about the CUDA ecosystem that helps developers solve real-world challenges. Learn the fundamentals of CUDA C++ with a collection of guided notebooks. Learn CUDA Python Get started with GPU development using Python with a collection of guided notebooks. How to Write a CUDA Program Learn about the CUDA ecosystem and how to write CUDA programs. Examples of How CUDA Is Used Today Artificial Intelligence Train a reasoning module using NVIDIA NeMo™ Framework and NeMo Curator. Blog: Train a Reasoning-Capable LLM in One Weekend With NVIDIA NeMo Blog: Train a Reasoning-Capable LLM in One Weekend With NVIDIA NeMo Code: NeMo Framework Code: NeMo Framework Notebook: Train Your Own Reasoning Model in 48 Hours Notebook: Train Your Own Reasoning Model in 48 Hours Artificial Intelligence Deploy AI models using NVIDIA Dynamo, an open-source, low-latency, modular inference framework. Blog: NVIDIA Dynamo, A Low-Latency Distributed Inference Framework for Scaling Reasoning AI Models Blog: NVIDIA Dynamo, A Low-Latency Distributed Inference Framework for Scaling Reasoning AI Models Guide: NVIDIA Dynamo Guide: NVIDIA Dynamo Training: Deploy LLM Inference With NVIDIA Dynamo and vLLM Training: Deploy LLM Inference With NVIDIA Dynamo and vLLM Computer-Aided Engineering AI-Powered CAE Simulations Accelerate your CAE simulations with CUDA-X-accelerated CAE tools, AI emulation, GPU acceleration, and real-time digital twins to design and build new technologies.

Blog: How to Run AI-Powered CAE Simulations Blog: How to Run AI-Powered CAE Simulations Guide: NVIDIA PhysicsNeMo™ Guide: NVIDIA PhysicsNeMo™ Training: Accelerating Computer-Aided Engineering (CAE) With NVIDIA AI Physics Technology Training: Accelerating Computer-Aided Engineering (CAE) With NVIDIA AI Physics Technology DataFrame and SQL Acceleration With cuDF cuDF is a GPU-accelerated library that optimizes fundamental DataFrame and SQL operations. It includes drop-in accelerators for popular DataFrame tools like pandas, Polars, and Apache Spark with no code changes required. Notebook: Build an Interactive Data Analytics Dashboard Notebook: Build an Interactive Data Analytics Dashboard Video: Accelerated Exploratory Data Analysis With pandas on NVIDIA GPUs (16:07 ) Video: Accelerated Exploratory Data Analysis With pandas on NVIDIA GPUs (16:07 ) User Guide: cuDF User Guide: cuDF User Guide: Apache Spark Accelerated With cuDF User Guide: Apache Spark Accelerated With cuDF Quantum Computing Accelerated Quantum Computing With NVIDIA CUDA-Q NVIDIA CUDA-Q™ is the quantum processing unit (QPU)-agnostic platform for accelerated quantum supercomputing. Blog: NVIDIA CUDA-Q Powers Quantum Applications Research Blog: NVIDIA CUDA-Q Powers Quantum Applications Research Training: CUDA-Q Academic Training: CUDA-Q Academic World Foundation Models With NVIDIA Cosmos Accelerate physical AI development for autonomous vehicles (AVs), robots, and video analytics AI agents. Blog: AI Foundation Models With NVIDIA Cosmos™ Predict-2 Blog: AI Foundation Models With NVIDIA Cosmos™ Predict-2 Github: Cosmos Predict-2 Github: Cosmos Predict-2 View Accelerated Computing Learning Path An Even Easier Introduction to CUDA An interactive accompaniment to Mark Harris's popular blog post “An Even Easier Introduction to CUDA.” Getting Started With Accelerated Computing in Modern CUDA C++ Learn how to write, compile, and run GPU-accelerated code. Accelerated Computing Hub Learn more about how to use CUDA in the Accelerated Computing Hub, which includes C++ and Python step-by-step tutorials and user guides. View Accelerated Computing Learning Path Get started with CUDA today. Download CUDA Toolkit