Unverified50% confidenceTradeoffExact time
Chip acceleration theoretically incurs no accuracy loss, whereas aggressive quantization may cause noticeable degradation on complex reasoning tasks.
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50%
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Long-term
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9/25/2026
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Verified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% similarUnverified即使固定PyTorch或TensorFlow的随机种子，若不设置torch.backends.cudnn.deterministic = True，计算结果仍可能因硬件调度差异无法完全复现73% similarUnverifiedIt is widely recognized in the industry that quantization below 2-bit leads to significant capability degradation on high-precision tasks such as complex multi-step reasoning and mathematical computation.72% similarUnverified现有的泛化理论大多假设性能随规模平滑变化，无法预测或解释涌现能力这种不连续的跃升72% similarUnverifiedIn ultra-long context inference scenarios, the performance bottleneck is often not compute but the KV cache itself.72% similar
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