Put inference where the workload needs it

A factory camera, a multilingual document service and a research batch job can all use AI. They do not require the same infrastructure or the same distance from the user.

Start with the response the application needs

For an interactive language service, measure the full path: request transport, queueing, preprocessing, first response and completion. For machine vision, include image capture, inference, decision and the machine interface. Record acceptable error rates and operating conditions alongside time. Fast incorrect output does not meet the requirement.

MLCommons provides workload-specific inference benchmark scenarios and quality constraints. Published benchmark results are useful evidence about a tested configuration. Your model, input distribution, concurrency and software may still require a representative acceptance run. MLPerf Inference methodology ↗

Local language is an application requirement

Sunbird AI documents a multilingual model covering Ugandan languages and English, including known limitations. It provides a concrete example of a regional workload whose quality must be evaluated in the languages and tasks actually served. Its published hardware guidance is a starting point for testing, not an SSA benchmark. Sunbird model documentation ↗

Build a representative evaluation set with permitted data. Include short and long inputs, uncommon terminology, low-quality audio where relevant and realistic concurrency. Preserve the model and software versions so a claimed improvement can be reproduced.

Choose among hosted, regional and on-site operation

A hosted service may suit variable demand and rapid experiments. Regional hosting may improve some network paths and simplify particular operating requirements. On-site inference may be appropriate where a process must continue through a lost external connection. Measure those properties rather than assuming that geographic proximity alone guarantees them.

A small installation can still require reliable power, cooling, security, spares and an accountable operator. National generation statistics cannot establish a site’s usable electrical capacity. Likewise, a provider’s broad edge network does not mean every point of presence offers equivalent GPU resources. Akamai separately describes its infrastructure footprint and its inference service. Akamai infrastructure ↗ · Cloud Inference announcement ↗

Size for measured useful output

Compare cost per accepted document, inspected item or completed request at the required quality and latency. Include quiet hours, bursts, failures and human fallback. A GPU-hour price without utilization and acceptance conditions is an incomplete economic measure.

Start with a bounded workload and a measured deployment. Increase capacity when operating evidence supports it. That approach can reveal a valuable local inference business without requiring an unproven data-centre build-out first.