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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 S3 Express One Zone
Amazon SageMaker Studio Lab
Amazon SageMaker Unified Studio
ApertureDB
DeepSpeed
EasyODM
Fabric for Deep Learning (FfDL)
Flyte
Gemma 3n
Gemma 4
Integrations Supported
Amazon S3 Express One Zone
Amazon SageMaker Studio Lab
Amazon SageMaker Unified Studio
ApertureDB
DeepSpeed
EasyODM
Fabric for Deep Learning (FfDL)
Flyte
Gemma 3n
Gemma 4
API Availability
Has API
API Availability
Has API
Pricing Information
$0
Free Trial Offered?
Free Version
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
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)
CAD/CAM Compatibility
Finite Element Analysis
Fluid Dynamics
Import / Export Files
Integrated 3D Modeling
Manufacturing Process Simulation
Mechanical Event Simulation
Multibody Dynamics
Thermal Analysis
Categories and Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization