Define the workload and limits
Describe how requests arrive, the approximate input and output length ranges, how concurrency may change, and what waiting time and error handling the application can tolerate. Separate known conditions from assumptions. Treat unsupported assumptions as questions to validate, not as capacity facts.
Split demand into user-relevant scenarios, such as ordinary traffic, an expected busy period, and a burst. For each, state which functions must remain available, what delay would disrupt the workflow, and whether requests may be queued, retried, or deferred by the user. Avoid starting with an assumed traffic limit for a particular model.
Write down how you will recognize an unacceptable outcome before running checks. For example, define whether a growing queue, repeated request failures, or delayed user-facing work should trigger a pause. The thresholds are application decisions to establish from your own requirements; do not treat example conditions as platform guarantees.
Prepare repeatable checks
For each scenario, prepare non-sensitive test inputs and record their approximate size, intended operation, concurrency schedule, and stop condition. Begin with a small run to verify the application path, error capture, and method for reviewing usage history. Increase load in deliberate steps, changing one major condition at a time so you can interpret differences.
If NexoToken's model API access is used, run checks through the same application call path intended for the workload. Account platform credits and usage history can help reconcile account-level consumption records. Consult the current main platform documentation for available operations and interface details; do not infer undocumented endpoints, rate limits, or capacity commitments.
Keep test inputs representative without exposing user data or secrets. Record any differences between the test setup and production, such as request mix or background work. If the check cannot reproduce an important part of the expected workload, mark that gap explicitly rather than treating the run as representative.
Record observations and investigate failures
For each run, capture the date and time, scenario, input sizes, concurrency settings, completed and failed request counts, waiting-time distribution, error categories, and retry behavior. Note environment changes and test limitations. Report only what this run actually showed; one run does not establish stable capacity or predict another application's results.
When errors rise, check plausible causes in a consistent order: request formatting, network conditions, application-side resource use, account credits, and concurrency configuration. These are investigation paths, not confirmed explanations. Compare application logs with usage history using the relevant time window and request correlation available in your own setup. If the records differ, note possible timing or correlation gaps and leave the discrepancy unresolved until verified.
If a run stops early, record the stop condition and the last available observations. Do not silently discard partial runs: they may help identify where a check became inconclusive, but they cannot establish behavior beyond the recorded interval.
Turn findings into a conditional plan
Write the plan as a conditional decision tied to a tested workload range: describe what the application observed within that range and what it should do beyond it, such as queue requests, limit incoming work, or ask users to retry later. If the sample is small, the workload is not representative, or a failure cause remains unclear, mark the result as unknown and schedule a focused follow-up check instead of filling the gap with an estimate.
For a hypothetical team concerned about busy-period backlog, the team could define an acceptable wait boundary, increase concurrency gradually, and record queue size, errors, and completion times at each step. This illustrates a procedure only; it is not a report of real outcomes or a claim that any system will behave in a particular way.
Repeat relevant checks after meaningful changes to the application, prompts, request flow, or workload pattern. Keep the prior records so later runs can be compared under documented conditions, and state clearly when changed conditions prevent a like-for-like comparison.
Sources & further reading
NexoToken platform and community
Platform website: https://www.nexotoken.net/
QQ community group: 822274386
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