Serving
Offline batch inference processes stored inputs without an interactive caller waiting for each response. Scheduling can favor aggregate throughput and completion efficiency over immediate token delivery.
Flexible deadlines allow larger batches, shape grouping, and deliberate retry policies. Those choices differ from interactive serving, where queue delay and token cadence are visible to users.
Workers read durable input partitions, group compatible shapes, and persist outputs with failure state. Operators balance batch efficiency against memory limits, fairness, and restart cost.
Offline batch inference processes stored inputs without an interactive caller waiting for each response. Scheduling can favor aggregate throughput and completion efficiency over immediate token delivery. Workers read durable input partitions, group compatible shapes, and persist outputs with failure state. Operators balance batch efficiency against memory limits, fairness, and restart cost.
Flexible deadlines allow larger batches, shape grouping, and deliberate retry policies. Those choices differ from interactive serving, where queue delay and token cadence are visible to users.
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