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KV Cache from Scratch in Python
Understanding the key optimization that makes LLM inference fast
TL;DR
KV caching avoids redundant computation during autoregressive generation by storing the key and value tensors from previous tokens. Without it, generating N tokens would require O(N²) attention computations. I'll implement a KV cache from scratch in pure Python/NumPy, then show how it integrates with a transformer decoder.