How do I support batch inference for large document jobs?

To support batch inference for large document jobs, design the system so documents are processed asynchronously, in chunks, and with retries/checkpointing rather than one huge synchronous request.

Core pattern

1) Split intake from processing

• Accept a job request with many documents or a large archive.
• Immediately return a job ID.
• Store the job metadata in a database or queue.
• Process documents in the background.

2) Chunk the work

Depending on your model and document type:

• Per document: each file becomes one task.
• Per page / section / chunk: for very large docs, split into smaller units.
• Per batch: group small documents to improve throughput.

A common approach is:

• ingest job
• extract text
• segment into chunks
• enqueue inference tasks per chunk
• aggregate results

3) Use a queue + worker architecture

Typical components:

• API service: receives jobs
• Object storage: stores files and intermediate artifacts
• Queue: SQS, Kafka, RabbitMQ, Redis queue, etc.
• Workers: run inference
• Result store: DB, blob storage, search index

This gives you:

• horizontal scaling
• backpressure
• retry handling
• isolation of failures

4) Make inference idempotent

Each unit of work should have:

• a stable task ID
• deterministic input versioning
• deduplication keys

So if a worker retries, you don’t double-write results.

5) Track job state carefully

Maintain:

• job_id
• document_id
• chunk_id
• status: queued, running, succeeded, failed
• attempts
• timestamps
• error message

Use checkpoints so a partially completed job can resume.

6) Aggregate results after all chunks finish

For large docs, you may need a final reduce step:

• combine chunk predictions
• merge entities
• reconcile overlaps
• run postprocessing

This is especially important if chunks overlap for context.

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Practical design options

Option A: Simple batch processing

Best when documents are moderate in size.

1. Upload documents
2. Enqueue one task per document
3. Worker runs inference
4. Store results
5. Mark job complete

Option B: Map-reduce pipeline

Best for huge docs or lots of docs.

1. Map: split each doc into chunks and infer independently
2. Reduce: merge chunk outputs into final doc output

Option C: Streaming pipeline

Best when documents arrive continuously.

1. Extract and chunk on the fly
2. Process each chunk as soon as available
3. Emit partial results early

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Important engineering considerations

Throughput and cost

• Batch requests where your model supports it
• Tune batch size based on latency and GPU memory
• Use dynamic batching if available

Model constraints

If the model has a token/context limit:

• chunk text by tokens, not just pages
• keep overlaps between chunks if needed
• preserve document structure metadata

Fault tolerance

• retries with exponential backoff
• dead-letter queue for poison jobs
• partial result persistence
• timeout limits per task

Observability

Track:

• queue depth
• worker utilization
• inference latency
• failure rates
• per-job progress

Data consistency

• write results atomically per chunk
• only mark job complete after all required chunks are persisted
• handle duplicate messages safely

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Example workflow

1. User uploads 5,000 PDFs.
2. API stores files and creates batch_job_123.
3. A splitter extracts text and creates 200,000 chunk tasks.
4. Workers pull chunk tasks and run inference.
5. Results are stored per chunk.
6. A reducer aggregates results per document.
7. Job status becomes complete.

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If you’re building with an ML model API

If your model endpoint supports batch inference:

• send a list of inputs in one request
• respect max batch size and token limits
• use async batch submission for long-running jobs
• poll for completion or use callbacks/webhooks

If it does not:

• implement batching at the application layer
• run many concurrent single-item calls with worker pools

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Recommended baseline architecture

A solid default stack is:

• API gateway/service
• Postgres for job metadata
• S3/GCS/Azure Blob for documents and outputs
• Redis/SQS/Kafka for task queueing
• worker autoscaling
• reducer service for final aggregation

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If you want, I can also provide:

1. a reference architecture diagram,
2. a database schema for batch jobs, or
3. sample pseudocode for the worker pipeline.