Unverified50% confidenceFactExact time
During training with mixed precision, memory usage roughly requires 16-20 bytes per parameter (including parameters, gradients, and optimizer states) plus activation overhead.
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11/1/2026
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UnverifiedDuring fine-tuning, model weights, gradients, and optimizer states must be stored simultaneously, making VRAM consumption typically 3–4 times that of inference.78% similarUnverifiedThe number of training epochs directly affects recognition performance; the more thorough the training, the higher the detection accuracy, and it is recommended to increase the number of training epochs to 20, 50, or even 10074% similarUnverifiedDeepMind's Chinchilla scaling law (from the 2022 paper "Training Compute-Optimal Large Language Models") shows that model performance depends on the optimal ratio of parameter count to training token count, with the optimal training volume being approximately 20 times the number of parameters74% similarUnverified批次归一化在训练模式使用当前批次统计量，在推理模式使用训练过程中累积的移动平均值74% similarUnverifiedTotal energy consumption on the inference side may exceed that of training, because it accumulates with user scale and invocation frequency.74% similar
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