How does KV caching improve ChatGPT inference performance in transformer architecture?

KV caching stores key and value tensors from previous tokens to avoid recomputation during autoregressive decoding.

In transformer inference, each new token requires attention over all previous tokens. Without optimization, the model recomputes key (K) and value (V) matrices for the entire sequence at every step, which is highly inefficient.

KV caching solves this by storing computed K and V tensors for previous tokens. At each new step, only the query (Q) for the latest token is computed, while past K and V are reused. This reduces complexity from O(n²) to O(n) per token generation step, significantly improving latency in ChatGPT-like systems.…