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硅谷一线从业者。AI架构师 AI architect
AI / 美股 /硅谷面试求职指导
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가입 March 2024
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KV 缓存是自回归 LLM 推理背后的基本优化。 Transformer 层存储早期 token 的键和值，然后在生成新 token 时重用它们。 这避免了重新计算，但以内存容量和带宽为代价。
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KV caching is the fundamental optimization underpinning autoregressive LLM inference.
Transformer layers store keys and values from earlier tokens, then reuse them as new tokens are generated.
This avoids recomputation, but at the cost of memory capacity and bandwidth.
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