AI Interview Series #4: Explain KV Caching

KV caching is an optimization technique in large language model (LLM) inference that stores previously computed key (K) and value (V) tensors during autoregressive text generation. By reusing these cached representations for earlier tokens, the model avoids redundant attention computations, significantly accelerating token generation as sequences grow longer. This approach addresses the inefficiency caused by recomputing attention over all previous tokens at each step, enabling faster inference without altering the underlying model architecture or hardware, though it requires additional memory to maintain the cache.