Best Embedded Software Development Tools for CUDA

Compare the Top Embedded Software Development Tools that integrate with CUDA as of July 2025

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This a list of Embedded Software Development tools that integrate with CUDA. Use the filters on the left to add additional filters for products that have integrations with CUDA. View the products that work with CUDA in the table below.

What are Embedded Software Development Tools for CUDA?

Embedded software development tools are used to write, compile, debug and deploy applications for embedded systems. These tools consist of a range of editors, compilers, debuggers and programming languages specifically designed for embedded computing. Most development tools are provided by embedded system vendors or third-party providers. There is also a variety of open-source and free tools available from various online sources. Embedded software development tools are an essential part of the embedded design process and allow developers to create efficient and reliable software designs for their projects. Compare and read user reviews of the best Embedded Software Development tools for CUDA currently available using the table below. This list is updated regularly.

1

MATLAB

The MathWorks

MATLAB® combines a desktop environment tuned for iterative analysis and design processes with a programming language that expresses matrix and array mathematics directly. It includes the Live Editor for creating scripts that combine code, output, and formatted text in an executable notebook. MATLAB toolboxes are professionally developed, rigorously tested, and fully documented. MATLAB apps let you see how different algorithms work with your data. Iterate until you’ve got the results you want, then automatically generate a MATLAB program to reproduce or automate your work. Scale your analyses to run on clusters, GPUs, and clouds with only minor code changes. There’s no need to rewrite your code or learn big data programming and out-of-memory techniques. Automatically convert MATLAB algorithms to C/C++, HDL, and CUDA code to run on your embedded processor or FPGA/ASIC. MATLAB works with Simulink to support Model-Based Design.

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2

NVIDIA Jetson

NVIDIA

NVIDIA's Jetson platform is a leading solution for embedded AI computing, utilized by professional developers to create breakthrough AI products across various industries, as well as by students and enthusiasts for hands-on AI learning and innovative projects. The platform comprises small, power-efficient production modules and developer kits, offering a comprehensive AI software stack for high-performance acceleration. This enables the deployment of generative AI at the edge, supporting applications like NVIDIA Metropolis and the Isaac platform. The Jetson family includes a range of modules tailored to different performance and power efficiency needs, such as the Jetson Nano, Jetson TX2, Jetson Xavier NX, and the Jetson Orin series. Each module is designed to meet specific AI computing requirements, from entry-level projects to advanced robotics and industrial applications.

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3

NVIDIA Isaac

NVIDIA

NVIDIA Isaac is an AI robot development platform that comprises NVIDIA CUDA-accelerated libraries, application frameworks, and AI models to expedite the creation of AI robots, including autonomous mobile robots, robotic arms, and humanoids. The platform features NVIDIA Isaac ROS, a collection of CUDA-accelerated computing packages and AI models built on the open source ROS 2 framework, designed to streamline the development of advanced AI robotics applications. Isaac Manipulator, built on Isaac ROS, enables the development of AI-powered robotic arms that can seamlessly perceive, understand, and interact with their environments. Isaac Perceptor facilitates the rapid development of advanced AMRs capable of operating in unstructured environments like warehouses or factories. For humanoid robotics, NVIDIA Isaac GR00T serves as a research initiative and development platform for general-purpose robot foundation models and data pipelines.

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