Unverified50% confidenceFactExact time
K-Quant是llama.cpp生态中的混合精度量化方案，根据每层权重敏感度分配不同量化bit数，可在相同平均bit数下将精度损失降低40-60%
1
Sources
50%
Confidence
Long-term
Relevance
8/16/2026
First Seen
Sources
Muse-Glimmer-30B实测：24G显存跑出九维跑分76分的性价比之王
Related Claims
UnverifiedQ4_K_M (4-bit quantization, K-means optimized) is currently the best overall choice, with a precision loss of approximately 2–5%, and is the most widely used quantization format in the community.75% similarVerifiedQ4_K_M中的K代表k-quants方案，M代表中等质量级别，是llama.cpp引入的混合量化策略73% similarUnverifiedQ6_K 使用 6-bit 量化并结合 K-quants 分组技术，相比 Q4 量化损失更小，相比 Q8 量化体积更小68% similarUnverifiedQ4_K_M采用分组量化策略，将权重张量划分为64或256维子块并独立计算缩放因子与零点，能将困惑度损失控制在1–3%以内67% similar
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