Workload benchmarking & optimisation

We agree the application, dataset or permitted substitute, software versions and expected result before testing. For AI this may involve training progress or inference responsiveness; scientific applications may be assessed by time to a validated result. Dataset preparation and any restrictions on where it can be used are recorded in the engagement.

Each comparison keeps relevant conditions visible, including precision, input size, concurrency, batch settings and available resources. We distinguish peak specifications from measured application behaviour. A result on one model or dataset is not presented as a universal forecast for another workload, and configuration differences are documented alongside the numbers.

Measurements can examine CPU activity, accelerator use, memory pressure, storage waits and network behaviour. The aim is to identify where the application spends time and which change is worth testing next. Recommendations may involve configuration, data preparation or a smaller deployment rather than automatically increasing the hardware specification.

The deliverable describes the method, test environment, results and limitations, with scripts or instructions appropriate to the engagement. The same test can inform a procurement comparison or a later acceptance exercise. Hardware access and scheduling are agreed in advance, including any use of supplier or cloud facilities.

Fees depend on workloads, candidate configurations, data preparation, test duration and the depth of tuning. Paid compute time and specialist application licences are separate costs where required. Share the decision you need to make and the target you need to meet so the quotation can focus on a useful comparison.