Unverified60% confidenceBenchmarkExact time
Quantization methods such as GPTQ, AWQ, and SqueezeLLM can still maintain over 95% of the original model's performance at 4-bit precision.
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60%
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Long-term
Relevance
8/28/2026
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UnverifiedAWQ (Activation-aware Weight Quantization) typically outperforms GPTQ at 4-bit quantization.78% similarUnverified4Bit量化下模型的基准测试得分通常能保持原始精度的90%-95%77% similarVerifiedThe 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.76% similarUnverifiedGPTQ（2022年）和AWQ（2023年）能将70B参数的LLM量化到INT4精度，困惑度损失不到1%75% similarUnverifiedNVFP4量化方案相比BF16基线实现了1.5倍的推理加速，同时在top-1准确率上保留了92%到97%的水平75% similar
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