Unverified50% confidenceFactExact time
该测试采用KL散度（KLD）作为客观量化指标，衡量量化后输出分布与BF16参考分布的偏离程度，KLD越低说明量化造成的信息损失越小
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50%
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Long-term
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8/8/2026
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Related Claims
UnverifiedThe choice of K value in Top-K routing affects the trade-off between computational efficiency and expressive capacity — a smaller K leads to faster inference but may sacrifice expressiveness, and in practice K is typically set to 1 or 2.69% similarUnverifiedKL散度校准方法由NVIDIA TensorRT推广，在激活值分布存在长尾时比简单MinMax方法能取得显著更好的精度68% similarUnverified分布偏移意味着训练集与测试集之间的KL散度不为零，即两个分布之间存在可度量的信息差距68% similarUnverifiedTop-K的K值存在经典权衡：K太小会漏掉关键信息，K太大会引入噪声导致大模型答案跑偏67% similar
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