Get Started With CUDA
Get the CUDA Development Environment
CUDA Toolkit
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.
Write GPU-Powered Python
CUDA Python
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.
Use Prebuilt, Optimized Libraries
CUDA-X Libraries
NVIDIA CUDA-XTM, built on CUDA, is a collection of libraries that deliver dramatically higher performance across application domains, including AI and HPC.
Build, Debug, and Profile Software
Nsight Developer Tools
NVIDIA NsightTM 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.
Unlock Peak GPU Performance
CUDA Tile
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.
CUDA Fundamentals
What Is CUDA?
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 CUDA C++
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
LLM Training
Train a reasoning module using NVIDIA NeMoTM Framework and NeMo Curator.
Code: NeMo Framework
Artificial Intelligence
LLM Inference
Deploy AI models using NVIDIA Dynamo, an open-source, low-latency, modular inference framework.
Guide: NVIDIA Dynamo
Data Science
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.
User Guide: cuDF
User Guide: Apache Spark Accelerated With cuDF
Robotics
World Foundation Models With NVIDIA Cosmos
Accelerate physical AI development for autonomous vehicles (AVs), robots, and video analytics AI agents.
Github: Cosmos Predict-2
Github: Cosmos
Quantum Computing
Accelerated Quantum Computing With NVIDIA CUDA-Q
NVIDIA CUDA-QTM is the quantum processing unit (QPU)-agnostic platform for accelerated quantum supercomputing.
Docs: CUDA-Q
Training: CUDA-Q Academic
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.
Guide: NVIDIA PhysicsNeMoTM
CUDA Resources
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.