Fair queuing
Also known as Fair queuing, AI Fair queuing
Definition
Fair queuing is the serving concept concerned with fair queuing during AI inference.
What fair queuing means
Fair queuing is the serving concept concerned with fair queuing during AI inference. In a serving system, define the measurement boundary before collecting data. State whether the worker is warm, which model and precision are active, how batching is configured, and whether the request is streamed. These details determine whether two similar measurements describe the same operating condition.
How it appears in a workload
A team can compare a quiet run with a burst of representative requests and observe how fair queuing changes. Record relevant timestamps and runtime signals with the request rather than relying on one dashboard aggregate. A useful comparison includes request mix, arrival pattern, concurrency, and output limits. For routed traffic, retain the selected route so a change can be investigated rather than attributed to fair queuing without evidence.
Limitations and tradeoffs
It should be interpreted with error rate and user-visible latency because an isolated gain can move cost or tail behavior elsewhere. Use it with p50 and tail latency, useful token throughput, errors, memory pressure, and cost. The best configuration is the one that meets the application's reliability and responsiveness requirements under representative traffic.
How this relates to Weave
Weave Router comparisons can record fair queuing beside model, provider, workload, and request outcome. That context helps explain whether a routing change altered fair queuing, but Weave does not make an inference runtime faster by itself. Keep serving conditions visible when using these observations to compare complete AI tasks.
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