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K-quant 系列引入了分块量化策略，对权重矩阵中不同的 block 使用不同的缩放参数以减少量化误差

1

Sources

50%

Confidence

Long-term

Relevance

8/21/2026

First Seen

Sources

Unsloth Dynamic 3.0 量化方案详解：按层动态分配精度，本地跑大模型更聪明

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Unverifiedk-quant量化方案不会对所有张量一视同仁地进行4-bit量化，而是对最敏感的张量（如注意力层、词嵌入层）保留更高精度，对其余部分进行更激进的压缩74% similar Unverifiedllama.cpp的K-quants系列采用超块分组量化策略，相比早期均匀量化在同等压缩率下可降低约20-30%的困惑度损失74% similar Unverified分组量化将权重矩阵按固定大小（通常32或64个元素）分组，每组独立计算量化缩放因子以减少精度损失71% similar UnverifiedModern quantization algorithms such as GPTQ, AWQ, and k-quant intelligently select which layers retain higher precision to keep performance loss within acceptable bounds.70% similar Unverified现代量化方案如GPTQ、AWQ及GGUF格式的分组量化，通过逐层校准和异常值保护，可将模型体积缩减75%以上同时保留绝大部分推理能力69% similar

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