Comparison
Inference vs KV Cache
Inference 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 Inference
Inference comes up when the question is fundamentally about inference.
A ChatGPT response: one inference call per turn.
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 Inference and KV Cache?
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. 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 Inference vs KV Cache?
Inference is the right concept when you are focused on inference. KV Cache applies when you are focused on architecture.
Are Inference and KV Cache the same thing?
No. Inference is inference; KV Cache is architecture. They are related but address different parts of the AI stack.