Comparison
Continuous Batching vs KV Cache
Continuous Batching and KV Cache 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 KV Cache
KV Cache comes up when the question is fundamentally about architecture.
Generating a 4K-token response: the KV cache fills up to 4K entries per layer.
Frequently asked
What is the difference between Continuous Batching and KV Cache?
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. KV Cache: The KV cache stores the key and value vectors of all earlier tokens during generation so the model does not recompute them at every step. It is the main memory cost of LLM inference.
When should I use Continuous Batching vs KV Cache?
Continuous Batching is the right concept when you are focused on inference. KV Cache applies when you are focused on architecture.
Are Continuous Batching and KV Cache the same thing?
No. Continuous Batching is inference; KV Cache is architecture. They are related but address different parts of the AI stack.