What Is NVIDIA Apollo and Why It Matters
NVIDIA has announced NVIDIA Apollo, a new family of open AI models built to supercharge industrial and engineering simulations. The reveal happened at the SC25 conference in St. Louis, and the goal is simple but bold: bring real time, AI powered physics to industries that usually rely on slow, expensive simulations.
Instead of waiting hours or days for traditional compute heavy simulations to finish, Apollo models aim to deliver near instant predictions. Think of them as AI physics engines that plug into existing tools and workflows.
The NVIDIA Apollo family is focused on real world engineering problems, including:
- Electronic device automation and semiconductors for defect detection, computational lithography, electrothermal behavior and mechanical design.
- Structural mechanics for analyzing stress and deformation in cars, airplanes and consumer devices.
- Weather and climate for global and regional forecasts, downscaling and advanced weather simulation.
- Computational fluid dynamics for airflow and fluid simulations in manufacturing, automotive, aerospace and energy.
- Electromagnetics for wireless communication, radar and high speed optical data systems.
- Multiphysics for complex setups like nuclear fusion, plasma simulations and fluid structure interaction.
Under the hood, Apollo uses modern AI architectures such as neural operators, transformers and diffusion models, combined with deep domain specific physics knowledge. These are not just generic models. They are tuned for engineering accuracy and scale.
NVIDIA will release pretrained checkpoints plus reference workflows for training, inference and benchmarking. Developers get a starting point they can customize for their own use cases instead of reinventing the wheel from scratch.
How Leading Companies Are Already Using AI Physics
Apollo is not launching into a vacuum. Many big names in chips, aerospace and simulation software are already working with NVIDIA AI physics models and infrastructure. Apollo gives them a more open, standardized way to build on that work.
Here is how some of the early adopters are putting AI physics to use.
- Applied Materials is using NVIDIA AI physics to design new materials and manufacturing processes for semiconductors. By running on NVIDIA GPUs and CUDA, its ACE Plus multiphysics software has seen up to 35 times speedups in some modules. Using data from ACE Plus, Applied has built surrogate AI models that can simulate flow, plasma and thermal behavior inside advanced process chambers in near real time. These are used for rapid design exploration and digital twins of manufacturing equipment.
- Cadence used its Fidelity Charles Solver with the NVIDIA powered Millennium M2000 supercomputer to generate thousands of detailed full aircraft simulations. That dataset trained an AI physics model for a real time digital twin of a complete aircraft. The result is an interactive model that reacts like a real plane but runs at speeds that traditional CFD could not touch.
- LAM Research is collaborating with NVIDIA to accelerate simulations of plasma reactors, which are key to etching and deposition in chip manufacturing. Faster and more accurate AI assisted simulations can help them design better reactors and processes faster.
- KLA plans to use Apollo models to push its semiconductor simulation and process control tools even further. By shortening simulation runtimes and improving accuracy, KLA can develop new inspection and control solutions at higher speed.
- Northrop Grumman and Luminary Cloud teamed up to speed up spacecraft thruster nozzle design. Using NVIDIA CUDA X accelerated CFD, Northrop Grumman generated a huge training dataset, then trained a surrogate model hosted on Luminary Cloud and powered by NVIDIA AI physics. Engineers can now explore thousands of thruster designs in the time it used to take to explore only a few.
- PhysicsX runs an AI native platform that covers the full AI lifecycle from simulation and data management to training, fine tuning and deployment. It integrates with NVIDIA AI physics infrastructure and simulation tools like Siemens Simcenter X. For industries such as automotive, aerospace and energy, the platform helps cut product development cycles and speeds time to market.
- Rescale is folding NVIDIA Apollo models into its AI physics operating system. Engineers on Rescale will be able to combine traditional high fidelity simulations with AI surrogates in one environment. That means exploring massive design spaces and getting real time inference while staying close to the accuracy of classic physics solvers.
- Siemens is bringing NVIDIA AI physics into Simcenter STAR CCM Plus and other flagship fluid simulation tools. Users will be able to mix first principles simulations with Apollo based AI models to test far more design options in a fraction of the time.
- Synopsys is using NVIDIA AI physics to stack GPU acceleration with AI surrogates and has reported up to 500 times speedups in some computational engineering workloads. Tools like Ansys Fluent can start from AI generated initial conditions instead of traditional estimates, slashing overall simulation runtime.
Where To Get NVIDIA Apollo and What Comes Next
NVIDIA positions Apollo as open, flexible and developer friendly. The models will land on familiar platforms, so teams do not have to rebuild their stack just to try them out.
NVIDIA Apollo models will be available soon on:
- build dot nvidia dot com for direct access to models and reference workflows.
- Hugging Face for easy integration into existing AI pipelines and experimentation.
- NVIDIA NIM microservices for deploying Apollo models as scalable services in production environments.
For developers and engineers, Apollo is basically a power up for simulation workflows. Whether you are modeling airflow over a wing, heat flow in a chip or plasma in a fusion reactor, Apollo aims to turn overnight batch jobs into something closer to interactive design. Expect faster iteration, bigger design spaces explored and tighter loops between simulation, AI and real world testing.
If you want to follow the rollout, NVIDIA offers a sign up option to be notified when Apollo models go live. For teams already deep into computational engineering, this is one of those transitions that can change how products are designed and tested over the next few years.
Original article and image: https://blogs.nvidia.com/blog/apollo-open-models/
