Scientific computing is a broad field covering everything from computational physics and fluid dynamics, to molecular modelling, genomics, and large-scale engineering simulations. Workloads in this space can stress nearly every part of a system; from raw CPU power to memory bandwidth, GPU acceleration, and fast storage throughput.

Whether you’re running finite element analysis (FEA), solving complex numerical models, or building custom research code, your workstation should be optimised to handle the unique characteristics of your workload. While vendor “system requirements” can be useful, they’re often outdated or too generic. The reality is that configuring the right workstation requires balancing compute, memory, and storage around the specific applications you run.

For many scientific workloads, the CPU remains the central performance driver. Simulation-heavy applications like computational chemistry, physics, or CFD often scale across many cores, but not infinitely.

AMD Threadripper PRO 9995WX / 9975WX

Extremely high core counts, excellent memory bandwidth, and support for 8-channel memory.

AMD Epyc 9004 Series

Exceptional for very large problems, with up to 12 memory channels per CPU.

Intel Xeon W-3500

Enterprise-class reliability, AVX-512 support, and new AMX instructions beneficial for mixed precision workloads.

• For well-scaling applications: 32–64 cores can offer excellent performance.

• For memory-bound codes: fewer cores with higher clocks may perform better.

• A good rule of thumb: pair cores with sufficient memory bandwidth (4–8GB RAM per physical core).

Many scientific applications now have GPU-accelerated versions that deliver orders-of-magnitude speed-ups compared to CPU-only execution. However, not all codes support GPU use, so your investment should align with your software stack.

• NVIDIA RTX 5090 / 5080 – Excellent FP32 performance for GPU-accelerated codes.

• NVIDIA RTX 6000 Blackwell – Up to 96GB VRAM, ideal for large datasets or visualisation workloads.

• NVIDIA H200 / L40S (Compute GPUs) – For applications requiring strong double precision (FP64) performance.

How much VRAM do I need?

• Entry-level GPU compute or visualisation: 12–16GB VRAM.

• Medium workloads (molecular dynamics, smaller CFD models): 24–48GB VRAM.

• Large-scale simulation or imaging datasets: 48–96GB VRAM.

• Extreme cases (HPC workloads, quantum chemistry): 141GB VRAM (H200 NVL).

Do I need multiple GPUs?

Only if your application scales across them (via CUDA-aware MPI, Horovod, etc.). If it does, 2–4 GPUs can rival small supercomputers. If not, invest in a single, higher-memory GPU.

Scientific codes are often memory-hungry. Large simulations or mesh-based solvers can easily consume hundreds of gigabytes of RAM, and memory bandwidth frequently becomes the bottleneck.

Recommended RAM

• Minimum: 64GB – suitable for lighter simulation or research workloads.

• Professional: 256–512GB – common for FEA, CFD, or molecular dynamics.

• High-end: 1TB+ – for extremely large datasets or enterprise-scale research.

Rule of thumb: At least 2× system RAM vs. total GPU VRAM to avoid bottlenecks when running GPU-accelerated applications.

Fast storage is critical for loading simulation data, writing results, and managing large datasets.

Recommended setup:

• Primary (OS & software): 1TB NVMe SSD.

• Secondary (working data): 2–8TB NVMe SSD or high-end SATA SSD.

• Tertiary (archive/backup): Large-capacity HDDs (12TB+) or NAS.

Some codes also require “scratch space” for temporary data during computations. In those cases, a dedicated NVMe drive is ideal. For larger research groups, network-attached or software-defined storage may be a better long-term solution.