Model parallelism
Also known as Model parallelism, AI Model parallelism
Definition
Model parallelism is the serving concept concerned with model parallelism during AI inference.
What model parallelism means
Model parallelism is the serving concept concerned with model 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
An interactive coding request can record model parallelism together with prompt size, output size, route, and timestamps. 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 model parallelism without evidence.
Limitations and tradeoffs
The result depends on prompt shape, output length, concurrency, batching, and hardware. 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 model parallelism beside model, provider, workload, and request outcome. That context helps explain whether a routing change altered model 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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