Batch inference
Also known as Batched inference, Inference batching
Definition
Batch inference processes multiple model inputs together in one serving operation. Grouping requests can improve hardware utilization, but it may add waiting time while a batch fills and must account for different input and output lengths.
Why serving systems batch requests
Accelerators can often use their compute more efficiently when several inputs are processed together. A serving system may collect requests for a short interval, form a batch, and execute the model with that group.
This pattern is common for offline jobs such as classifying a dataset or generating embeddings. It can also be used for online traffic when the service sets a small batching window and keeps interactive delays within an acceptable range.
The tradeoff is visible in latency
If a request arrives just after a batch is formed, it may wait for the next one. Requests with different context or output lengths can also create uneven completion times. Measure queue time, time to first token, completion time, and throughput together.
Dynamic batching policies can limit the maximum batch size or waiting window. Those settings should be part of a reproducible benchmark, because changing them can move the system to a different balance of capacity and responsiveness.
Choose it for the workload
Batch inference is a strong fit when many independent inputs can tolerate scheduled processing. Interactive coding-agent turns often need tighter latency bounds. Test representative requests and report both the efficiency benefit and the user-visible wait.
How this relates to Weave
Batching can change the latency and cost profile of an AI workflow. Weave Router comparisons should use the serving conditions that resemble the target workload, especially when agent requests are interactive and cannot wait indefinitely for a full batch.
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