CUDA Platform for Accelerated Computing

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.

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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.

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Use Prebuilt, Optimized Libraries

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.

Explore CUDA-X Libraries

Build, debug, and profile software

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.

Explore Nsight Tools

CUDA Fundamentals

CUDA Programming Guide

NVIDIA CUDA platform for accelerated computing — Click Image to Enlarge

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.

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Learn CUDA C++

Learn the fundamentals of CUDA C++ with a collection of guided notebooks.

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Learn CUDA Python

Get started with GPU development using Python with a collection of guided notebooks.

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How to Write a CUDA Program

Learn about the CUDA ecosystem and how to write CUDA programs.

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Examples of How CUDA Is Used Today

Artificial Intelligence

LLM Training

Train a reasoning module using NVIDIA NeMo™ Framework and NeMo Curator.

Blog: Train a Reasoning-Capable LLM in One Weekend With NVIDIA NeMo
Code: NeMo Framework
Notebook: Train Your Own Reasoning Model in 48 Hours

Artificial Intelligence

LLM Inference

Deploy AI models using NVIDIA Dynamo, an open-source, low-latency, modular inference framework.

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.

Notebook: Build an Interactive Data Analytics Dashboard
Video: Accelerated Exploratory Data Analysis With pandas on NVIDIA GPUs (16:07)
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.

Blog: AI Foundation Models With NVIDIA Cosmos™ Predict-2
Github: Cosmos Predict-2
Github: Cosmos

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
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.