Pipeline parallelism
Also known as Pipeline parallelism, AI Pipeline parallelism
Definition
Pipeline parallelism is the serving concept concerned with pipeline parallelism during AI inference.
What pipeline parallelism means
Pipeline parallelism is the serving concept concerned with pipeline parallelism 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 pipeline parallelism 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 pipeline parallelism 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 pipeline parallelism beside model, provider, workload, and request outcome. That context helps explain whether a routing change altered pipeline parallelism, 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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