Inference Batching: Grouping Requests for Throughput

Why it exists

Processing inference requests one by one is inefficient. A GPU can often handle much more data than a single request provides, leaving expensive hardware underutilized. This sequential processing creates a bottleneck, limiting throughput and increasing the average latency for a queue of users.

The mental model

Think of it as a carpool lane for your ML model. Instead of sending every person (an inference request) in their own car, you have them wait briefly at a pickup point. You then fill a bus (a large batch) and send it through the fast lane (the GPU). The bus might take slightly longer to complete its trip than a single car, but you move far more people per hour, which is the definition of higher throughput.

How it works

An inference server, like NVIDIA Triton, intercepts incoming requests. Instead of sending each one directly to the model, it holds them in a queue for a configurable, very short delay (e.g., 100 microseconds). During this window, it gathers other requests and combines them into a single, larger tensor. This "dynamic batch" is then sent to the model for one efficient processing run. Afterwards, the server de-batches the results and sends them back to the individual clients. This behavior is typically configured on a per-model basis.

When to use it

Use dynamic batching for stateless models serving high-traffic applications. It's most effective when you receive many small, concurrent requests, such as in a public-facing API for an LLM, a translation service, or an image recognition endpoint. The primary goal is to maximize hardware throughput and cost-efficiency.

When not to use it

It's less effective for stateful models where the context of one request depends on another. It's also not beneficial for applications with very low, sporadic traffic, as there won't be enough concurrent requests to form meaningful batches. In ultra-low-latency, single-stream applications where even microsecond delays are unacceptable, you might disable it.

One canonical example

A model supports a maximum batch size of 8. Five requests arrive with individual batch sizes of 4, 2, 2, 6, and 2. Without batching, processing them sequentially requires 5 separate model executions. With dynamic batching, an inference server can group them. For example, it could combine the first and third requests (4+2=6), the second and fourth (2+6=8), and process the last one alone (2). This reduces five separate executions down to three, significantly increasing throughput by better utilizing the GPU's parallel processing capabilities.