KV Cache Explained: Why It's Crucial in LLM Inference and Often Misunderstood
techNmak · x · 2026-09-06
The author points out that KV cache is one of the most important concepts in LLM inference, yet often explained too casually. During autoregressive generation, without caching, each decoding step would recompute key and value states for already processed tokens. KV caching avoids redundant work by storing and reusing past K/V tensors.
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