Unverified50% confidenceBenchmarkExact time
Mainstream quantization schemes typically cause only a 1–3% performance loss at 4-bit quantization precision, but performance degradation becomes significantly more pronounced at 2-bit quantization.
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9/10/2026
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VerifiedModern quantization algorithms can keep the performance loss of INT4 quantization within 1–3%.80% similarUnverifiedPost-training quantization solutions such as GPTQ and AWQ can control performance loss to within 1–2% at INT4 precision75% similarUnverifiedVector quantization always faces the fundamental challenge of a precision-efficiency trade-off: the higher the compression ratio, the greater the quantization error and the lower the recall rate.74% similarVerified量化会不可避免地引入精度损失，在逻辑推理等对准确性敏感的任务上可能出现性能下降74% similarVerifiedModel compression techniques such as INT8 quantization and channel pruning can increase inference speed by 2–4 times with a precision loss of less than 1% mAP74% similar
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