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What is RunMat?
RunMat eliminates MATLAB license costs while delivering faster performance. Teams can run existing .m scripts and workflows without rewriting code or purchasing per-seat licenses.
GPU acceleration is automatic -- no code changes required. Engineers get results up to 131x faster than NumPy on dense numerical workloads, accelerating parameter sweeps, simulations, and data processing pipelines.
Deploys anywhere: desktop (Windows, macOS, Linux), browser (no install needed), CI/CD pipelines, and Jupyter notebooks. Single portable binary with no dependencies. MIT licensed with no seat limits.
Ideal for engineering teams in signal processing, controls, quantitative finance, imaging, and scientific computing who need high-performance numerical tools without proprietary license overhead.
What is PyTorch?
Seamlessly transition between eager and graph modes with TorchScript, while expediting your production journey using TorchServe. The torch-distributed backend supports scalable distributed training, boosting performance optimization in both research and production contexts. A diverse array of tools and libraries enhances the PyTorch ecosystem, facilitating development across various domains, including computer vision and natural language processing. Furthermore, PyTorch's compatibility with major cloud platforms streamlines the development workflow and allows for effortless scaling. Users can easily select their preferences and run the installation command with minimal hassle. The stable version represents the latest thoroughly tested and approved iteration of PyTorch, generally suitable for a wide audience. For those desiring the latest features, a preview is available, showcasing the newest nightly builds of version 1.10, though these may lack full testing and support. It's important to ensure that all prerequisites are met, including having numpy installed, depending on your chosen package manager. Anaconda is strongly suggested as the preferred package manager, as it proficiently installs all required dependencies, guaranteeing a seamless installation experience for users. This all-encompassing strategy not only boosts productivity but also lays a solid groundwork for development, ultimately leading to more successful projects. Additionally, leveraging community support and documentation can further enhance your experience with PyTorch.
Integrations Supported
Amazon EC2 P4 Instances
Amazon SageMaker Debugger
Cameralyze
Deeplake
Exafunction
Flower
Fuzzball
Gemma 3n
Groq
HStreamDB
API Availability
API Availability
Pricing Information
$0
Free, Open Source. MIT licensed runtime with no seat limits. Cloud plans available for collaboration and team features.
Free Version
Pricing Information
Pricing not provided
Supported Platforms
SaaS
Windows
Mac
On-Prem
Linux
Supported Platforms
Android
iPhone
iPad
Windows
Mac
Linux
Customer Service / Support
Web-Based Support
Customer Service / Support
Web-Based Support
Training Options
Documentation Hub
Training Options
Documentation Hub
Webinars
Company Facts
Organization Name
RunMat
Date Founded
2022
Company Location
United States
Company Website
runmat.com
Company Facts
Organization Name
PyTorch
Date Founded
2016
Company Website
pytorch.org
Categories and Features
Computer-Aided Engineering (CAE)
Fluid Dynamics
Import / Export Files
Manufacturing Process Simulation
Mechanical Event Simulation
Thermal Analysis
Categories and Features
AI Development
Not specified
AI/ML Model Training
Not specified
Machine Learning
Not specified
Neural Network
Not specified