How to choose a GPU cloud region for inference

Choosing a GPU region by map distance is a guess. The useful comparison is how your application performs from the places where people use it, alongside whether the compute you need is available when you need it.

Hostnot GPU’s catalog lists 142 offerings across 24 GPU types and 8 regions, alongside 49 serverless models. Those counts describe the breadth of the catalog. They do not provide an inference latency comparison or a live allocation forecast for each region. You need to measure those things against your own workload.

Start with the workload and the user locations

Write down where requests originate, what each request does, and what response time your application can tolerate. A chat request, image job and video request can have different payload sizes and processing times. Compare like with like: same input, same model or runtime, and the same application-side steps.

Choose a small set of user locations that represent real traffic, rather than trying to test every possible network. If your users are spread across several areas, keep the measurements separate. An average can hide a slow path that matters to one group.

Next, decide whether you need a GPU instance or serverless inference. Instances provide an SSH-accessible environment for workloads that need a custom runtime or more control. Serverless is suited to supported request-and-response workflows. If you are weighing those approaches, the hybrid workflow distinction between SSH instances and serverless calls is a useful starting point.

Compare instance regions with a repeatable test

For an instance-based service, identify candidate regions in the customer-visible GPU catalog and check whether the required offering is available. Match the GPU and configuration to the job first. A region with a faster network path is not a practical choice if it cannot supply the compute your workload requires.

Deploy the same test service in each candidate region, where capacity permits. From each user location, send repeated requests using representative inputs. Record the full round-trip time at the client, not just the time spent inside the model. Also record errors and the time needed to obtain an instance. These are separate measurements: a responsive running instance does not guarantee that a new allocation will be available on demand.

Run the test more than once and at different times. Availability and rates can change as capacity synchronizes, so a single successful deployment is not a capacity plan. Keep the test small, avoid sending sensitive production data, and remove test resources when finished.

Use percentiles as well as averages when you review results. The median describes a typical request; a high percentile helps show whether occasional slow responses could disrupt the application. Keep network round-trip, application overhead and model processing time separate where your instrumentation allows. Otherwise, you may move regions to solve a delay that is actually inside your service.

Measure serverless from the application path

For serverless inference, measure requests from the same application or client locations where production traffic begins. Hostnot GPU’s serverless API supports chat, image and video requests. Use a model and request shape that match the intended job, and compare repeated end-to-end timings and failures.

Do not infer regional routing from the number of regions in the GPU catalog. The available facts do not establish that a serverless request can be assigned to a chosen region, or that a model is served from a particular one. Check the current model listing and API contract before designing around regional pinning. If there is no documented region choice, measure the route the API actually provides rather than claiming a region-specific result.

Model selection matters too: a different model can change processing time enough to make a network comparison misleading. Matching a serverless model to the task and its API contract helps keep that part of the test controlled.

Turn measurements into an allocation decision

Choose a primary region only after checking both user-facing performance and the availability of the required offering. For instance workloads, note how often the needed configuration can be deployed during your test window. For serverless workloads, record the measured endpoint behavior and any documented placement controls. Do not treat a catalog count as a promise of capacity at a particular moment.

If one region is clearly faster for the largest user group and has workable availability, it may be the simplest starting point. If results are close, prioritize dependable access to the needed configuration and keep the deployment design easy to change. A second region is useful only if your application can route to it and the platform supports the deployment pattern you intend to use.

Keep a dated record of the test inputs, request counts, user locations, selected offerings and observed results. Repeat it when traffic shifts, the workload changes or availability changes. That turns “global GPU capacity” from a map-based assumption into an operational decision grounded in your own latency and allocation evidence.