A Guide to GPU-Accelerated CAE and the Cost-Performance Benefits

GPU-accelerated computing is one of the most transformative trends in modeling and simulation today. All of the major engineering software providers are supporting GPU architectures in addition to traditional CPUs, and for good reason. 

Whether you’re running finite element analysis (FEA), computational fluid dynamics (CFD), particle simulations (DEM), or electromagnetic analysis (EM), leveraging GPUs can lead to massive speedups and cost savings. Performance gains depend on factors like solver capabilities, physics complexity, model size, and hardware configuration, but even conservative benchmarks consistently show dramatic improvements. In many real-world cases, for GPU-accelerated solvers simply switching hardware delivers 5× to 20× speedups and lowers the cost per-simulation.

Of course, those gains only matter if you have access to the right GPUs—and your software can take advantage of them.

But how would you know if your solver is compatible, and which GPUs are meant for computer-aided engineering (CAE) workloads? And if you make the switch, what kind of results should you expect?

Let’s break it down.

With a GPU, How Fast is Fast?

GPUs have earned a strong reputation for accelerating performance in compute intensive workloads. Typically the trend has been for new software releases and new GPU architectures to consistently deliver incremental performance gains.

However, NVIDIA GTC 2025 marked a turning point. Driven by AI advancements, engineers are now experiencing orders of magnitude performance gains sharing achievements and benchmarks that would have been unimaginable just a few years ago.

For example, Ansys has integrated NVIDIA’s cuDSS library within its HFSS (High-Frequency Structure Simulator) electromagnetics solver, leveraging GPU acceleration to achieve up to an 11x speed improvement.

Still wondering how fast is fast? Tasks that once took hours—or even days—now run in minutes. This is why leading software providers across CAE including Ansys, Altair, Cadence, Siemens, and Synopsys are partnering with NVIDIA to reset performance expectations and redefine what’s possible.

What Does GPU-Accelerated Really Mean?

When a solver is described as “GPU-accelerated,” it means the software has been developed to offload visualization or computation tasks to a GPU, rather than relying solely on the CPU. 

Acceleration generally falls into two key categories: graphics acceleration, which enhances visual rendering and display performance, and solver acceleration, which speeds up complex computational workloads like simulations, machine learning, or data analysis. 

Visualization: Graphics Acceleration

In the high performance computing space, Elastic Cloud Workstations (ECWs) that use Desktop Cloud Visualization (NICE DCV) are a widely adopted solution for secure, high performance remote visualization.

ECWs provide low-latency access to GPU-enabled virtual desktops, allowing engineers and researchers to interact with graphics-intensive applications such as Abaqus/CAE, ANSYS Workbench, or Siemens STAR-CCM+ directly from the cloud.

Remote visualization, or virtual machines, are ideal for pre- and post-processing tasks, where your laptop’s GPU (like a NVIDIA Quadro) can accelerate operations such as model rotation, zooming, and animation playback, delivering a smooth and responsive user experience, even from a lightweight local machine.

Modeling and Simulation: Solver Acceleration

Some engineering and scientific solvers are built to offload core numerical computations—such as matrix assembly, sparse matrix factorization, and linear and iterative solvers—onto GPUs for faster execution. 

Common applications of GPU acceleration in CAE include:

• Sparse matrix factorization
• Conjugate gradient solvers
• Explicit dynamics steps
• Particle and Lagrangian methods
• High-fidelity CFD meshing and turbulence modeling

Each year, software vendors expand GPU support to cover more physics and simulation applications. Ansys Fluent 2025 R1 now accelerates combustion, particle, and radiation models on GPUs, while Simcenter STAR-CCM+ 2502 boosts performance in thermal and battery simulations with GPU-native solvers.

Will Any GPU Do for Cloud HPC?

The short answer is no.

Some GPUs are intended for the consumer or gaming market, optimized for graphics and single-precision workloads. Others, particularly in the HPC space, are designed for AI inference and large language models (LLMs), prioritizing tensor operations over double-precision math.

 3 GPU Must Haves for CAE:

• Double Precision (FP64): Accurate and stable solvers in CAE require FP64 support, especially for large-scale simulations where numerical precision is critical.
• High Memory Bandwidth & Capacity: Large models and complex meshes require fast data transfer and plenty of GPU memory. For serious CAE workloads, aim for bandwidth over 600 GB/s and at least 24 GB of memory.
• CUDA Support: Most CAE software relies on NVIDIA’s CUDA for GPU acceleration, making it essential for compatibility with leading solvers and custom GPU code.

GPU-accelerated CAE solvers rely on low-level, GPU-optimized libraries like NVIDIA’s cuBLAS, cuSPARSE, and cuSolver. Built to exploit the massively parallel architecture of modern GPUs, these libraries are finely tuned making them far more efficient than traditional CPU-based libraries like the BLAS for large-scale numerical computations.

These libraries need 64-bit precision and a lot of memory to handle the high precision heavy math workloads efficiently. This is why CAE workloads typically run on compute-class GPUs like the NVIDIA V100, A100, H100, and RTX series, which are built for high parallelism and floating-point performance.

To understand GPU performance for CAE, Rescale GPU HPC experts routinely test and maintain a maturity index for hardware that tracks multi-cloud availability, performance and costs. Rescale experts benchmark and validate new architectures before they are widely available, ensuring only production-ready core types are recommended.

Which CAE Solvers Are GPU-Enabled?

The easiest way to check if your solver is GPU-enabled is to consult the official release notes or documentation of your software.

GPU support is expanding rapidly, below we’ve compiled a non-exhaustive list of leading engineering solvers with GPU support. 

• ANSYS Mechanical
• ANSYS Fluent
• ANSYS Rocky
• ANSYS HFSS
• Dassault Systèmes Abaqus
• Dassault Systèmes CST
• Siemens STAR-CCM+
• Cadence Fidelity
• Cadence Spectre
• Hexagon (MSC) Marc
• Hexagon (MSC) Nastran
• Hexagon (MSC) Actran 

For batch runs, GPU-related settings and version details are typically available in the command-line options or logs. For details on specific versions or assistance with running them using GPU flags, feel free to connect with our Rescale GPU HPC experts.

The Business Case for GPUs, Why it Matters

GPU acceleration isn’t just about speed. It’s about solving problems that were previously out of reach.

NASA for example, has been a pioneer in GPU-native acceleration. Available on Rescale, their CFD codes can afford to model everything all at once: aerodynamics, heat transfer, combustion, acoustics, and structural stress—all tightly coupled multi-physics. We are talking full-vehicle hypersonic CFD or re-entry heat shields with billions of cells.

Think back to the last time you gave up on running a simulation, whether due to time limits, memory bottlenecks, or compute cost constraints. High-fidelity CFD (RANS/LES), large-scale crash simulations, advanced multiphysics, and DEM workloads all push CPU hardware to the limits.

In many of these cases, GPU acceleration can deliver results in hours instead of days.Demand for high-end GPUs will only continue to grow—so don’t wait. Strategic workload planning and resource allocation is critical to ensure your engineering team has the right availability and capacity.

When you’re ready to make the switch from CPUs to GPUs, Rescale’s experts can help profile your workloads and identify the most efficient GPU configurations that optimize both runtime and cost.