Why Your LLM Doesn't Re-Read the Prompt: The KV-Cache
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The KV-cache is the single most important optimisation in LLM inference — and the reason real-time chat with a model is even feasible. Here's what it is and why it matters. Generation is autoregressive An LLM produces text one token at a time: emit a token, append it, run the whole model again for the next. Inside each attention layer, every token becomes a Query, a Key, and a Value. To produce the newest token, its Query is scored against the Keys of all previous tokens, and those weights…
1Key Takeaways
- The KV-cache is the single most important optimisation in LLM inference — and the reason real-time chat with a model is even feasible.
- Here's what it is and why it matters.
- Generation is autoregressive An LLM produces text one token at a time: emit a token, append it, run the whole model again for the next.
- Inside each attention layer, every token becomes a Query, a Key, and a Value.
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that the KV-cache is the single most important optimisation in LLM inference — and the reason real-time chat with a model is even feasible.
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