NVIDIA PhysicsNeMoNVIDIA
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NVIDIA TensorRTNVIDIA
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Related Products
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About
NVIDIA PhysicsNeMo is an open source Python deep-learning framework for building, training, fine-tuning, and inferring physics-AI models that combine physics knowledge with data to accelerate simulations, create high-fidelity surrogate models, and enable near-real-time predictions across domains such as computational fluid dynamics, structural mechanics, electromagnetics, weather and climate, and digital twin applications. It provides scalable, GPU-accelerated tools and Python APIs built on PyTorch and released under the Apache 2.0 license, offering curated model architectures including physics-informed neural networks, neural operators, graph neural networks, and generative AI–based approaches so developers can harness physics-driven causality alongside observed data for engineering-grade modeling. PhysicsNeMo includes end-to-end training pipelines from geometry ingestion to differential equations, reference application recipes to jump-start workflows.
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About
NVIDIA TensorRT is an ecosystem of APIs for high-performance deep learning inference, encompassing an inference runtime and model optimizations that deliver low latency and high throughput for production applications. Built on the CUDA parallel programming model, TensorRT optimizes neural network models trained on all major frameworks, calibrating them for lower precision with high accuracy, and deploying them across hyperscale data centers, workstations, laptops, and edge devices. It employs techniques such as quantization, layer and tensor fusion, and kernel tuning on all types of NVIDIA GPUs, from edge devices to PCs to data centers. The ecosystem includes TensorRT-LLM, an open source library that accelerates and optimizes inference performance of recent large language models on the NVIDIA AI platform, enabling developers to experiment with new LLMs for high performance and quick customization through a simplified Python API.
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Platforms Supported
Windows
Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Audience
Researchers, engineers, and developers who need an open source Python AI framework to build, train, fine-tune, and deploy physics-informed machine learning models for simulation, digital twins, and real-time prediction
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Audience
Machine learning engineers and data scientists seeking a tool to optimize their deep learning operations
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Support
Phone Support
Supported
24/7 Live Support
Not Supported
Online
Supported
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Support
Phone Support
Supported
24/7 Live Support
Not Supported
Online
Supported
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API
Offers API
Supported
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API
Offers API
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Screenshots and Videos |
Screenshots and Videos |
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Pricing
Free
Free Version
Supported
Free Trial
Not Supported
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Pricing
Free
Free Version
Supported
Free Trial
Not Supported
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Reviews/
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Reviews/
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Training
Documentation
Supported
Webinars
Supported
Live Online
Supported
In Person
Not Supported
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Training
Documentation
Supported
Webinars
Supported
Live Online
Not Supported
In Person
Supported
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Company InformationNVIDIA
Founded: 1993
United States
developer.nvidia.com/physicsnemo
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Company InformationNVIDIA
Founded: 1993
United States
developer.nvidia.com/tensorrt
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Categories |
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Integrations
PyTorch
Supported
Python
Supported
CUDA
Not Supported
Dataoorts GPU Cloud
Not Supported
Kimi K2
Not Supported
Kimi K2.5
Not Supported
Kimi K2.6
Not Supported
Kimi K2.7 Code
Not Supported
Kimi K3
Not Supported
LaunchX
Not Supported
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Integrations
PyTorch
Supported
Python
Supported
CUDA
Supported
Dataoorts GPU Cloud
Supported
Kimi K2
Supported
Kimi K2.5
Supported
Kimi K2.6
Supported
Kimi K2.7 Code
Supported
Kimi K3
Supported
LaunchX
Supported
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