Unverified60% confidenceFactTime unknown
The perplexity loss of Q4_K_M quantization compared to FP16 is typically within 1%–3%
2
Sources
60%
Confidence
Long-term
Relevance
6/1/2026
First Seen
Sources
本地部署大模型怎么判断显存爆了？一文看懂显存监控方法
bilibili步步梅
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UnverifiedQuantization error has a non-uniform distribution characteristic; the Q/K/V matrices of the attention mechanism and the last few layers are more sensitive to quantization.73% similarUnverifiedQuantization typically brings a 1%–3% accuracy loss, but for discriminative tasks with a limited label space such as intent classification it is almost imperceptible.73% similarVerifiedQuantization below Q4 (such as Q3 or Q2) causes excessive loss of weight information on coding tasks, leading to a sharp decline in output quality.71% similarUnverifiedIn the homogeneous missingness setting (where each covariate is observed with independent probability q), the error bound derived by the authors exhibits a complex rather than standard dependence on q.70% similarVerifiedModern quantization schemes (such as Q4_K_M and Q5_K_M) employ grouped quantization and mixed-precision strategies, typically keeping accuracy degradation within 1–3%.70% similar
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