This is an illustrative scenario, not an account of a specific client engagement. Consider a structural engineering firm in Abuja running ANSYS Mechanical for finite element analysis on complex structural projects. Their workstation is an ageing Xeon-based machine that has served them well but is increasingly falling behind on solve times as project complexity grows.
ANSYS is one of the most CPU-intensive applications in professional use. Solve times on complex models can range from minutes to hours depending on mesh density and analysis type. Every percentage point of CPU performance improvement translates directly to reduced turnaround time on deliverables.
The Configuration
- Intel Core i9-14900K (ANSYS Mechanical benchmarks favour high-frequency cores for many solver types — the i9's boost clocks matter here)
- 128GB DDR5 RAM (large FEA meshes require substantial memory; running out causes paging that destroys solve times)
- NVIDIA RTX 4070 Ti Super 16GB (GPU-accelerated solvers in ANSYS can dramatically reduce solve times on supported analysis types)
- 2TB NVMe PCIe 5.0 (result file I/O during solve operations)
- 1000W 80+ Platinum (the i9-14900K under full solver load)
Results
Moving from an ageing Xeon to a current high-frequency i9 with 128GB of RAM cuts solve times substantially on a workload like this — and the RAM matters as much as the CPU, because an FEA mesh that does not fit in memory pages to disk, and paging does not slow a solve down so much as ruin it.
The threshold worth aiming at is qualitative: solves that force you to leave work running overnight versus solves that finish inside the working day. Benchmark a candidate build against your own representative model — that is the only number that means anything for your practice.