Unverified50% confidenceBenchmarkExact time
The performance gap between a carefully quantized 4-bit model and the original FP16 model is typically within 1–3 percentage points.
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8/30/2026
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VerifiedThe larger the number of model parameters, the greater the robustness to quantization, allowing 70B-level models to maintain performance close to FP16 after 4-bit quantization.78% similarUnverified对于7B至13B参数级别的模型，4-bit量化后的输出质量与全精度版本差异很小76% similarUnverifiedQuantization methods such as GPTQ, AWQ, and SqueezeLLM can still maintain over 95% of the original model's performance at 4-bit precision.73% similarUnverifiedCompared to FP16 original precision, FP8 quantization typically causes no more than 1–2% performance degradation on mainstream benchmarks.73% similarUnverified研究表明当模型参数规模达到3B以上时，1.58-bit模型在多项基准测试中表现可接近甚至匹配同等规模的全精度模型72% similar
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