Unverified50% confidenceFactExact time
SteerTTS-edge's weights are dequantized back to fp32 on load, so the benefit of int4 quantization is size reduction rather than inference speedup.
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10/22/2026
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VerifiedQuantizing a model from FP32 to INT8 can improve inference speed by 3–4x, but may lead to reduced detection accuracy for small targets.69% similarUnverifiedWhen evaluating quantization schemes, one should look at the number of qualified outputs per unit time rather than simply single-inference speed, as the speed gains from INT4 may be offset by the overhead of repeated regeneration.69% similarVerified混合精度训练将权重保存为FP32，而前向和反向传播计算使用FP16或BF16，可将显存占用减少近一半67% similarUnverifiedFP8动态范围较窄，在反向传播和梯度累积时容易出现数值溢出或下溢，因此训练侧actor保持BF16精度66% similarUnverifiedQuantization precision (e.g., FP16, INT8, INT4) directly affects the memory footprint and computational efficiency of model weights.66% similar
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