Unverified50% confidenceFactExact time
The prefill stage is compute-bound, requiring all input tokens to be processed at once to generate the KV Cache, with attention matrix computation complexity of O(n²).
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50%
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Long-term
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9/10/2026
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Related Claims
Unverified引入KV Cache可将自回归生成的计算复杂度从O(N²)降为O(N)75% similarUnverifiedSpeculative decoding improves inference speed by using a small draft model to generate candidate tokens that are verified in parallel by the main model; sparse attention reduces computational complexity from O(n²) to near O(n).72% similarUnverifiedKV Cache技术允许模型在自回归生成时复用历史token的注意力Key-Value矩阵计算结果，使长对话推理延迟从O(n²)降低至接近线性71% similarUnverified自注意力机制的QK^T计算对于序列长度n、隐藏维度d的模型需要O(n²d)次乘加运算69% similarVerifiedThe autoregressive generation process of reasoning models is strictly sequential; KV Cache VRAM usage grows linearly with the length of the thinking token sequence, making memory bandwidth the performance bottleneck.68% similar
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