HPC and AI

Boost Accuracy with GPU-Accelerated HPC and AI

Accelerated computing is helping researchers accomplish their scientific breakthroughs faster. But researchers are quickly realizing that AI can help them produce high-accuracy results that are on par with scientific simulations in a much shorter time frame. This has fueled the adoption of AI in high-performance computing (HPC).


Who Uses HPC and AI?

HPC and AI can be used in a variety of  fields, including researchers working in laboratories, engineers solving complex technical problems, and financial analysts using mathematical algorithms to make market predictions.

HPC simulations with AI


Researchers are enhancing their HPC simulations with AI to achieve faster and better results for various scientific workloads.

Engineers are using AI to evaluate a variety of designs


Engineers are using AI to evaluate a variety of designs, including medical devices, manufacturing robots, and automotive components.

Analysts at financial organizations are leveraging AI


Analysts at financial organizations are leveraging AI to identify and predict market trends, flag fraudulent transactions, and speed up online payments.

Accelerate Your Workloads

NVIDIA enables HPC researchers to take advantage of AI with GPU-optimized AI and HPC software—available from the NVIDIA NGC™ catalog—that can be deployed on GPU-powered HPC clusters, cloud instances, and workstations.


PyTorch is a GPU-accelerated tensor computational framework. Functionality can be extended with common Python libraries such as NumPy and SciPy.


TensorFlow is an open-source platform for machine learning. It provides comprehensive tools and libraries in a flexible architecture that allows easy deployment across a variety of platforms and devices.


TorchANI is a PyTorch implementation of ANI and contains classes like AEVComputer, ANIModel, and EnergyShifter that can be pipelined to compute molecular energies from the 3D coordinates of molecules.

To explore the performance speedups of some key HPC applications, visit the NVIDIA Developer Zone. To get started with these GPU-accelerated applications, visit NVIDIA NGC

Unlock Industrial and Scientific Simulation Capabilities with NVIDIA SimNet

AI-Accelerated Simulation Toolkit

NVIDIA SimNet is a physics-informed neural network (PINNs) toolkit that addresses the challenges of using AI and physics. Whether you're looking to get started with AI-driven physics simulations or working on complex nonlinear physics problems, NVIDIA SimNet can help you solve forward, inverse, or data assimilation problems.

NVIDIA SimNet™ - AI-Accelerated Simulation Toolkit

HPC and AI in Action

HPC and AI have many applications, such as solving our earth’s climate problems, accelerating scientific discoveries, and simulating workflows to complete tasks faster.

 NVIDIA SimNet Toolkit

Simulate Workflows for Product Designs

NVIDIA SimNet is an end-to-end AI-driven simulation framework based on a novel PINN architecture. SimNet helped solve a multi-physics problem to perform automatic design space exploration 1,000X faster than traditional simulation, with the accuracy of numerical solvers.

HPC and AI are used in many geosciences scenarios

Model Earth Systems

HPC and AI are used in many geosciences scenarios, including extreme weather forecasting, physics emulation, nowcasting, medium-range forecasting, uncertainty quantification, bias correction, generative adversarial networks, data in-painting, network-HPC coupling, PINNs, and geoengineering, among others.

NGC catalog offers containers for the latest versions of AI, HPC

Take Advantage of HPC and AI Software from NGC

The NGC catalog offers containers for the latest versions of AI, HPC, and visualization software.

Accelerate Scientific Discoveries

Accelerate Scientific Discoveries

The convergence of deep learning and artificial intelligence with traditional HPC accelerates the pace of scientific discovery, from high-energy physics to life sciences and healthcare.

Learn more about HPC and AI through session and demo videos, or get started with the NVIDIA Developer Blog.