NVIDIA PhysicsNeMo compatible GPU workstation
A GPU environment for PhysicsNeMo that integrates physical laws and AI to accelerate design verification.
NVIDIA PhysicsNeMo is a physics AI framework that combines physical laws and AI to accelerate simulations of fluids, heat, structures, and more. This product is a GPU workstation that allows for comfortable execution of learning, inference, and what-if validation of PhysicsNeMo in a local environment. It offers a balanced model equipped with an AMD Ryzen 9 and RTX PRO 4500 Blackwell, as well as a large-scale analysis model featuring a Threadripper and RTX PRO 6000 Blackwell Max-Q. It adopts Ubuntu 24.04 LTS and supports development environments utilizing PyTorch, Docker, Google Colab, and Magnum IO. It allows for quick verification of the results of design variable changes, supporting the efficiency of design exploration and digital twin development.
basic information
**Ryzen 9 Model** CPU: AMD Ryzen 9 9950X3D (16 cores / 32 threads) Memory: 128GB DDR5-5600 SSD: 2TB M.2 NVMe GPU: NVIDIA RTX PRO 4500 Blackwell 32GB OS: Ubuntu 24.04 LTS Power Supply: 850W 80PLUS GOLD 1-year send-back warranty **Threadripper Model** CPU: AMD Ryzen Threadripper 9970X (32 cores / 64 threads) Memory: 256GB DDR5-5600 ECC RDIMM SSD: 4TB M.2 NVMe GPU: NVIDIA RTX PRO 6000 Blackwell Max-Q 96GB OS: Ubuntu 24.04 LTS Power Supply: 1200W 80PLUS PLATINUM 3-year send-back warranty Customization is available to match your usage and analysis scale.
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Applications/Examples of results
It can be utilized for fluid analysis, thermal analysis, structural analysis, aerodynamic design, cooling design for electronic devices, design optimization for vehicles and aircraft, digital twins, robotics, and simulation of manufacturing processes. For numerous design proposals that require significant computation time in conventional CFD and CAE, PhysicsNeMo allows for the construction of surrogate models, enabling quick estimation of results after changing conditions. For example, it is suitable for comparing temperature distributions while changing heat sink shapes or flow conditions, exploring aerodynamic characteristics due to changes in body shape, and estimating physical quantities such as pressure, velocity, and temperature at near-real-time speeds. It can support everything from proof of concept (PoC) to actual operation in research and development departments, universities and research institutions, and design and analysis departments in manufacturing.
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