Unverified50% confidenceBenchmarkExact time
GGUF格式的Q4_K_M是最主流的平衡选择，困惑度损失通常低于1%；Q8_0几乎无精度损失但文件体积是Q4的两倍
1
Sources
50%
Confidence
Long-term
Relevance
7/11/2026
First Seen
Sources
Ollama Raises $65M Series B: 85% of Fortune 500 Have Already Deployed Local LLMs
redditr/ollama7/10/2026
Related Claims
VerifiedGGUF量化格式（如Q4_K_M）通过将权重精度从32位浮点压缩到4位整数，通常只带来约1-5%的性能损失81% similarUnverifiedQ4_K_M has a perplexity approximately 0.1–0.3 lower than pure Q4.81% similarUnverifiedQ4_K_M往往是本地部署时性价比最高的首选方案，实测困惑度损失通常不超过1%77% similarUnverifiedIn the Q4_K_XL scheme, MoE layers use MXFP4 and the remaining layers are compressed to Q8_0, achieving 96% same on top-1% prediction consistency74% 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72% similar
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