Verified65% confidenceOpinionExact time
Training frontier large models requires enormous computational investment, and there are diminishing marginal returns as model scale increases.
3
Sources
65%
Confidence
Long-term
Relevance
9/11/2026
First Seen
Sources
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UnverifiedPausing large model training is costly, involving enormous compute expenditures and potentially affecting the pace of product iteration and competitive market position.80% similarUnverifiedTraditional large model training relies heavily on manually annotated data and external corpus collection, which are typically the bottlenecks constraining iteration speed.79% similarVerifiedTraining a frontier large model requires hundreds of millions of dollars in compute, data cleaning, and human labor.79% similarUnverified大模型的工程落地分为训练和推理两个核心环节79% similarVerifiedThe energy consumption during the training phase of large models is approximately hundreds of times that of the inference phase.78% similar
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