Verified75% confidenceFactExact time
Research indicates that at Q4 precision and above, performance loss is within 1–3% for most tasks.
5
Sources
75%
Confidence
Long-term
Relevance
7/23/2026
First Seen
Sources
用 Ollama 跑本地模型接入 Claude Code，AI 成本降低 99%
Related Claims
UnverifiedPrecision loss comparison by quantization level: Q5 is nearly lossless, Q4 has approximately 2%–5% loss, Q3 has approximately 8%–15% loss, and Q2 has approximately 30%–50% loss.80% similarUnverified研究表明在INT4/INT8精度下多数推理和生成任务的输出质量下降幅度在1-3%以内79% similarUnverifiedThe higher the quantization level (e.g., Q4), the less VRAM is consumed, but generation quality will decrease accordingly.76% similarVerifiedFor models with more than 30B parameters, the performance loss caused by 4-bit quantization typically results in a 1–3% drop on benchmarks, but the impact is more significant for smaller models.75% similarUnverifiedWhen using the multi-agent verification process, MARCH's accuracy was suppressed to 4.4%, whereas when the same model was directly asked which solution was better, accuracy reached 43.7%.75% similar
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