Comparison
Decoder vs KV Cache
Decoder 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 Decoder
Decoder comes up when the question is fundamentally about architecture.
GPT-4 generating a paragraph token by token.
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 Decoder and KV Cache?
Decoder: A decoder is a transformer module that generates a sequence one token at a time, using causal self-attention so each token only sees earlier ones. GPT-style LLMs are decoder-only. 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 Decoder vs KV Cache?
Decoder is the right concept when you are focused on architecture. KV Cache applies when you are focused on architecture.
Are Decoder and KV Cache the same thing?
No. Decoder is architecture; KV Cache is architecture. They are related but address different parts of the AI stack.