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The Noise Equivalent Hypothesis is proposed: the noise inherent to the weight quantization process plays a role functionally analogous to the stochastic gradient variance present in standard SGD optimization, and it is theoretically prove that just as a decaying learning rate is essential for convergence, an ascending...
Training Large Foundation Models (LFMs), including Large Language Models and Vision-Language Models, on massive distributed GPU clusters is increasingly bottlenecked by communication overhead. While frameworks like ZeRO++ employ static quantization to reduce communication volume, they suffer from a rigid trade-off: agg...
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