As we continue to grow in the field of technology for computational sciences, selecting the right hardware for your computational science workload remains a struggle for many consumers. NVIDIA has been a market giant in the world of graphics cards for over 30 years, supplying key GPUs for a variety of computational science workloads.
Procuring the right accelerator may seem daunting at first, but the two most popular choices are the NVIDIA RTX 5090 and the NVIDIA H100, each varying in differences. In this blog, we will break down which GPU is the best fit for your intended workload and how Ace Computers helps customers make the right decision.
With AI advancing across technologies, GPUs’ role in computational science workloads has become even more critical to how your hardware can run your operations. Models are becoming much larger, and meshes are more complex, reinforcing the need for GPUs. GPUs have become the center of high-performance computational science largely due to their parallel architecture, which processes thousands of data points at once, making GPUs imperative for large matrices, particle interactions, nonlinear solvers, and real-time visualizations.
Here are five ways GPUs accelerate core computational science tasks:
Although CPUs are required for preprocessing, branching logic, and serial operations, modern GPUs like the NVIDIA RTX 5090 and NVIDIA H100 can handle a large quantity of numerical computation, reinforcing the importance of your GPU selection.
The decision between the NVIDIA RTX 5090 and NVIDIA H100 comes down to a deep understanding of your computational science workload intentions. The question to ask is,
“Do you need workstation-class GPU acceleration for simulation and visualization, or HPC-grade parallel compute for large-scale computational science?”
While both of NVIDIA’s top-line GPUs are powerhouses, the NVIDIA RTX 5090 is fit for high-frequency, visualization-heavy, GPU-accelerated tasks, and the NVIDIA H100 is built for large-scale parallel computing, AI-inference, and HPC workloads.
The NVIDIA RTX 5090 is built for those who require extreme GPU performance in a workstation environment. This GPU excels in simulation, visualization, and GPU-accelerated engineering tools, which depend on high clock speeds, strong CUDA performance, and real-time responsiveness. Customers can consider this GPU an excellent selection for modeling and visualization.
The NVIDIA RTX 5090 is built for applications like:
NVIDIA RTX 5090 Performance Highlights Include:
Why Buy an NVIDIA RTX 5090?
For those whose workloads are dependent on interactive modeling, GPU-accelerated solvers, or real-time visualization, the NVIDIA RTX 5090 is the perfect match.
NVIDIA’s H100 accelerator is engineered for high-performance computing, AI-accelerated research, and multi-GPU parallel workloads. The NVIDIA H100 is commonly referred to as the “standard” for large-scale environments that require maximum throughput, memory bandwidth, and cluster-level scaling. To classify the NVIDIA H100, this GPU is the perfect enterprise-class HPC accelerator for large parallel computing and AI.
The NVIDIA H100 is built for applications like:
NVIDIA H100 Performance Highlights Include:
Why buy an NVIDIA H100?
If you are someone whose workloads require parallel compute, multi-GPU scaling, or AI-accelerated simulation, there is no avoiding the fact that the NVIDIA H100 is the most efficient available GPU for the given computational science workloads.
| Feature | NVIDIA RTX 5090 | NVIDIA H100 |
|---|---|---|
|
Positioning |
Workstation-class GPU |
HPC-class accelerator |
|
Use Cases |
Simulation, visualization, modeling |
Large-scale parallel compute, AI, HPC |
|
Workload Scale |
Single-GPU or small-scale |
Multi-GPU, multi-node |
|
Memory Bandwidth |
High |
Extremely High |
|
Tensor Performance |
Strong |
Very Strong |
|
Ideal Applications |
ANSYS, COMSOL (GPU modules), CAD, GROMACS (small) |
GROMACS (large), CFD, FEM, AI pipelines |
|
Platform |
Choose the NVIDIA RTX 5090:
If you are looking for workstation-class performance for simulation, modeling, and visualization, then the RTX 5090 is the right GPU for you. You will be able to run ANYSY, COMSOL, or GROMACS on a single GPU while getting the maximum performance per dollar.
Choose the NVIDIA H100:
If you run large-scale computational science or HPC workload loads, then the H100 is the right GPU for you and all your multi-GPU scaling needs. If you are looking to work with AI-accelerated simulation or surrogate modeling, then this GPU is built to deliver the maximum throughput for long-duration, parallel workloads.
While the decision may seem easy once you understand your first desired workload, it’s critical to possess a deeper wealth of knowledge about your scale and your long-term compute strategy for the future. This is where Ace can help play a big role. Ace Computers specializes in helping customers with the decision-making process when it comes to hardware procurement.
For over 40 years, Ace has excelled in delivering high-performance workstations and HPC systems for federal agencies, research labs, higher education, and enterprise engineering teams. Our expertise ranges from both workstation-class GPU platforms to HPC-grade multi-GPU clusters, allowing our team to give customers a unique guide to the right architecture for their computational science environment.
How Ace Helps You Make the Right Decision:
As technology continues to grow, so will the requirements for computational science solutions, and as workloads get larger, the need for the right hardware cannot be overstated enough. Your system needs the right GPU to keep up with others and applications that will continuously be updated.
Both the NVIDIA RTX 5090 and H100 are premier selections when it comes to choosing a GPU for your computational science workload. The NVIDIA RTX 5090, being ideal for simulation, modeling, and visualization, and the H100 is fit for AI-acceleration and modeling.
If you have any questions about which GPU is the best fit for your workload or would like to speak to an expert, please contact us today.
