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Measure duration within a defined clock domain. Do not subtract timestamps from different hosts without verified synchronization assumptions.
Requests have client, proxy and engine timestamps. Subtracting one from another looks like a way to obtain precise queue or network time, but clocks can differ in origin, resolution and adjustment. Even within a process, wall time can change independently of execution. Providers need a measurement schema separating duration from calendar time and identifying every observation boundary.
Choose a duration clock
Python time documents perf_counter and monotonic duration measurement. It supports differences between events within a consistent process clock domain. Its absolute value should not become a calendar timestamp or a universal scale across machines.
Record clock and unit in instrumentation. If a library returns nanoseconds, convert to seconds explicitly. Unit mistakes can create plausible charts at the wrong scale, so a small known-wait fixture helps before inference workloads begin.
Keep wall time for correlation
Calendar timestamps locate logs and relate them to deployments, but do not replace monotonic duration. Both can belong in a record with distinct purposes. Exporter updates must not change field meaning silently while retaining the same metric name.
OpenTelemetry context propagation links distributed operations through context. This helps locate related spans but does not synchronize clocks. A shared trace ID does not make every cross-host timestamp difference an accurate transfer measurement.
Define request events
Name submission, local API acceptance, processing start, first useful event and completed reading. Identify each observation location. Client waiting and server execution answer different questions and can include different stages.
Prometheus instrumentation guidance emphasizes consistent operation counting and latency measurement. Completion-based requests are easier to compare with failures and durations when boundaries align. Do not mix start-based counts with end-based duration records without understanding short-window effects.
Test incompatible clocks
A synthetic fixture can contain two local durations and two deliberately offset wall timestamps. Expected analysis should preserve correct local intervals while refusing an unverified decomposition of total time. This tests analytics without changing a real server’s system clock.
Where cross-host analysis is necessary, record synchronization assumptions and uncertainty. Do not display decimals implying precision beyond the measurement. Leaving an unknown segment explicit is better than assigning it to GPU time from incompatible clock scales.
Version the measurement schema
After client, proxy-path or instrumentation changes, repeat a short control request. Compare metric definitions rather than values alone. If first-event time now means headers instead of content, apparent improvement may be semantic rather than actual model acceleration.
Related guides cover benchmark methodology and GPU monitoring. The deliverable is an explicit clock-and-event schema for honest comparison without invented precision or claims about an unknown infrastructure’s latency. Retain the known-wait fixture and expected unit conversion with the schema so a later refactor can detect measurement drift before it reaches operational dashboards.