Batching
Continuous batching schedules work at each model iteration instead of waiting for an entire request batch to finish. Finished sequences leave the active batch while ready sequences enter without restarting the serving loop.
This keeps accelerator slots useful across requests with different prompt and output lengths. Higher utilization can lower cost per token, but crowded batches can increase queueing and tail latency.
A serving scheduler admits new sequences between decode steps and removes sequences that have completed. Operators tune batch limits against memory pressure, latency targets, and workload shape.
Continuous batching schedules work at each model iteration instead of waiting for an entire request batch to finish. Finished sequences leave the active batch while ready sequences enter without restarting the serving loop. A serving scheduler admits new sequences between decode steps and removes sequences that have completed. Operators tune batch limits against memory pressure, latency targets, and workload shape.
This keeps accelerator slots useful across requests with different prompt and output lengths. Higher utilization can lower cost per token, but crowded batches can increase queueing and tail latency.
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