Adaptive batching
Also known as Adaptive batching, AI Adaptive batching
Definition
Adaptive batching is the serving concept concerned with adaptive batching during AI inference.
What adaptive batching means
Adaptive batching is the serving concept concerned with adaptive batching 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 gateway trace can connect adaptive batching with the selected model, provider, queue, and completion outcome. 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 adaptive batching without evidence.
Limitations and tradeoffs
This term describes one serving property, not model quality or a promise that every request behaves identically. 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 adaptive batching beside model, provider, workload, and request outcome. That context helps explain whether a routing change altered adaptive batching, 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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