Comparison
KV Cache vs Self-Attention
KV Cache and Self-Attention are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
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.
When you would reach for Self-Attention
Self-Attention comes up when the question is fundamentally about architecture.
In a sentence about a pronoun, self-attention links "it" to its antecedent.
Frequently asked
What is the difference between KV Cache and Self-Attention?
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. Self-Attention: Self-attention is attention applied within a single sequence: each token attends to every other token in the same input, including itself.
When should I use KV Cache vs Self-Attention?
KV Cache is the right concept when you are focused on architecture. Self-Attention applies when you are focused on architecture.
Are KV Cache and Self-Attention the same thing?
No. KV Cache is architecture; Self-Attention is architecture. They are related but address different parts of the AI stack.