Comparison
Continuous Batching vs Inference
Continuous Batching and Inference are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Continuous Batching
Continuous Batching comes up when the question is fundamentally about inference.
A vLLM server: 200 concurrent users with variable-length responses; GPU utilization stays at 95% vs ~30% on static batching.
When you would reach for Inference
Inference comes up when the question is fundamentally about inference.
A ChatGPT response: one inference call per turn.
Frequently asked
What is the difference between Continuous Batching and Inference?
Continuous Batching: Continuous batching lets new requests join an in-flight batch on the next decode step rather than waiting for the current batch to finish, dramatically raising GPU utilization on variable-length workloads. Inference: Inference is what happens when you actually run a trained model on new input. For LLMs that means generating tokens one at a time, with sampling and a KV cache.
When should I use Continuous Batching vs Inference?
Continuous Batching is the right concept when you are focused on inference. Inference applies when you are focused on inference.
Are Continuous Batching and Inference the same thing?
No. Continuous Batching is inference; Inference is inference. They are related but address different parts of the AI stack.