What Is Inference Batching? Processing Multiple Requests Together
Running a model on requests one at a time wastes a lot of its capacity — batching groups several together to use hardware far more efficiently.
Grouping requests to use hardware efficiently
Inference batching groups multiple incoming requests together and processes them through the model at the same time, rather than handling each one entirely separately — taking advantage of a GPU's ability to do many parallel calculations at once instead of leaving most of that capacity idle.
Why processing one at a time wastes capacity
A GPU processing a single request at a time typically uses only a small fraction of its available parallel compute capacity — most of the hardware sits idle. Batching several requests together fills that idle capacity, dramatically improving overall throughput for a given amount of hardware.
The latency tradeoff
Waiting to accumulate a full batch before processing adds a small delay to individual requests, so serving systems have to balance batch size against acceptable response latency — larger batches improve efficiency but risk making an individual user wait longer for their specific response.
Frequently Asked Questions
What is inference batching?
Grouping multiple incoming requests together and processing them through a model simultaneously, rather than handling each one separately, to make much better use of a GPU's parallel compute capacity.
Does inference batching slow down individual requests?
It can add a small delay, since a request might wait briefly for a batch to fill before processing starts — a deliberate tradeoff for significantly better overall throughput and hardware efficiency.