High-Volume Batch Inference
Our infrastructure layer supports asynchronous batch inference for workloads where total throughput matters more than immediate response time.
Appropriate workloads
Appropriate workloads can include archive analysis, nightly monitoring, classification, extraction, and large-scale processing. These jobs can involve thousands or millions of independent records that do not need to block an interactive user while they are processed.
Batch execution
- Dataset / work queue
- Partition
- Schedule
- Batch inference
- Validate
- Persist result / retry failures
Separating batch traffic from interactive traffic allows each path to optimize for a different service objective. Interactive serving generally prioritizes response latency. Batch execution can prioritize hardware utilization, throughput, and predictable completion of a large workload.
Throughput tuning
Batch size can improve accelerator utilization, but larger batches consume more memory and can increase recovery cost when one portion fails.
Scheduling also matters. A nightly monitoring job may be able to consume spare local compute that would be inappropriate for daytime interactive traffic. Archive backfills may be rate-limited to avoid starving recurring production workloads.
Failure isolation
A long-running batch should not need to restart from zero because one record fails. Work can be partitioned and checkpointed so invalid inputs, transient inference failures, or downstream write errors are isolated and retried according to their failure class.
The system should also preserve which model and configuration processed each batch when reproducibility matters.
Tradeoff
Batch inference intentionally accepts delayed output. That trade is useful only when the workload can tolerate it. The advantage is the ability to process large volumes economically while maintaining clear scheduling, retry, validation, and completion state.
We use batch inference when throughput and unit economics are more important than per-request immediacy.
Reproducibility and lineage
Large batch runs can take long enough that models, prompts, or source data change while the job is executing. Where the output needs to be reproducible, the batch should preserve the versions and source boundaries that defined the run. This prevents a single logical batch from silently mixing outputs generated under different configurations.
Batch architecture can also separate permanent failures from transient ones. A malformed source record should not be retried indefinitely alongside temporary provider or hardware failures. The queue can route those classes differently and produce completion statistics showing processed, failed, retried, and quarantined work.
A batch that reports 99.9% complete can still be unacceptable if the missing 0.1% contains the highest-value or most difficult records.