What is batch inference? Grouped tasks still need individual reconciliation
Explain batch inference versus concurrency and reconcile identifiers, completion states and partial failures.
Understand the concept
Batch inference processes grouped inputs through combined computation or asynchronous batch services. It differs from concatenating prompts and need not block the user. Define independence and availability and match every input and output; a completed batch does not establish success for all records.
Compute batches and service jobs are different layers
PagedAttention studies serving memory and batching; OpenAI Batch defines job submission and retrieval. One visible job does not mean all inputs execute simultaneously. Concurrent ordinary calls do not inherit batch-service pricing or deadlines. Dependent tasks need prerequisites. Separate input grouping, backend computation and result retrieval rather than extending success at one layer to all.
Use identifiers to check successful, failed and missing items
Assign stable IDs to public classification inputs and reconcile successes, errors and omissions by ID, not order or line counts. Retry only necessary records and preserve results against duplicates. No batch or model call occurred here. Validate quality against checked examples; larger-scale processing does not make unsupported answers reliable.
Maintain scheduling and retention around deadlines
Offline work can tolerate delays while interactive users may need immediate replies. Update retrieval and recovery as retention or support changes. Count retries, reconciliation and corrections, not nominal unit prices. Save versions and result locations for restart recovery. Validate a small complete workflow before scale; delivery means usable complete results, not merely a successful job ID.
Checked sources
These sources support the concepts and methods described here. Examples and test scope are labeled separately; check the version of the tools you use.