Scientific and HPC workloads
Scientific computing has different needs from AI. Many codes rely on high-precision arithmetic, and some scale across nodes with tight communication.
What matters most
Numerical precision
Some simulations need high-precision formats. Check that a GPU’s strengths match the precision your code uses.
Software compatibility
Confirm your libraries and toolchain support the vendor and generation you plan to rent.
Interconnect needs
Tightly coupled codes need fast networking between nodes. Embarrassingly parallel ones do not.
Data residency
Research data may be subject to rules on where it can be processed.
A typical setup
Depends on the code. Benchmark a small case first, then scale to the nodes and network it actually needs.
How it is usually bought
Short allocations on demand for exploration, then committed capacity once the workload is understood.
Common mistakes
- Assuming AI-optimised GPUs excel at every precision.
- Skipping a small benchmark before committing to a large allocation.
- Overlooking data residency requirements.
This is general guidance, not a recommendation for a specific provider or price. Check current offerings and confirm availability and terms with the provider.
Ready to look at real options?
See tracked offerings with their sources and dates, or ask Kova to size it for you.