Continuous Batching vs. Static Batching in AI Inference

This article explains why continuous iteration-level batching dramatically increases throughput compared to static request batching in artificial intelligence inference servers. Large Language Models (LLMs) generate text sequentially, creating significant computational bottlenecks when traditional static batching is used. By shifting from request-level scheduling to iteration-level scheduling, continuous batching eliminates idle GPU time, optimizes memory allocation, and processes incoming requests without waiting for entire batches to finish. This transition is the primary driver behind the high throughput and low latency of modern AI production environments.

The Bottleneck of Static Request Batching

To understand the efficiency of continuous batching, it is necessary to look at how LLMs generate responses. LLM inference happens in two distinct phases: the prefill phase, where the input prompt is processed in parallel, and the decode phase, where output tokens are generated one by one in an autoregressive loop.

In static request batching, the inference server groups a set number of user requests together and processes them simultaneously. The fundamental flaw with this approach is that different requests require different output lengths. For example, in a batch of four requests, one request might finish generating its response at 20 tokens, while another might require 500 tokens.

Under static batching, the batch is locked until every single request within it is complete. The requests that finish early cannot exit the batch, and the GPU must pad their completed sequences with empty operations while waiting for the longest request to finish. During this time, the GPU's compute cores allocated to the finished requests sit completely idle, and new incoming requests are stuck in a queue.

How Continuous Iteration-Level Batching Works

Continuous batching—also known as dynamic or iteration-level batching—solves the idle GPU problem by changing how the server schedules work. Instead of making scheduling decisions at the request level, the server makes decisions at the iteration level (after every single token is generated).

When a batch is running under continuous batching, the server monitors the completion status of each sequence after every forward pass of the model. The exact moment a sequence generates its end-of-sequence (EOS) token, it is immediately evicted from the batch and returned to the user. In the very next iteration, the scheduler pulls a waiting request from the queue and injects it into the newly vacated slot in the active batch.

Why Throughput Dramatically Increases

The shift from static to continuous batching yields massive throughput improvements—often up to 10x or 20x in real-world scenarios—for three primary reasons:

1. Elimination of Padding and Idle Compute

Because finished sequences are removed immediately, the GPU no longer wastes compute cycles processing padded tokens for completed requests. Every slot in the batch is perpetually doing useful work, keeping GPU utilization near 100% as long as there is a queue of incoming traffic.

2. Interleaving Prefill and Decode Phases

The prefill phase of an LLM is compute-bound, while the token-by-token decode phase is memory-bandwidth-bound. Static batching forces all requests to go through the prefill phase together and the decode phase together. Continuous batching allows the server to interleave these phases. While existing requests in the batch are in their memory-bound decode phase, a newly injected request can execute its compute-bound prefill phase, maximizing the use of different hardware components on the GPU simultaneously.

3. Immediate Release of KV Cache Memory

During generation, LLMs store attention data in the GPU's high-bandwidth memory, known as the Key-Value (KV) cache. The KV cache grows with every token generated and is often the biggest bottleneck in scaling inference. In static batching, the memory held by a finished request is trapped until the longest request in the batch finishes. Continuous batching frees a request's KV cache the instant its final token is generated. This freed memory can immediately be allocated to new requests, allowing the server to run much larger overall batch sizes without running out of memory.