Unverified50% confidenceOpinionExact time
When training tasks are continuous and large in scale, the marginal cost of a self-built cluster may be lower than that of pay-as-you-go cloud services.
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50%
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Long-term
Relevance
10/26/2026
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UnverifiedTraining can be completed concentrated in a few supercomputing clusters, but inference is an ongoing expense, and lowering the cost per token of inference helps seize the initiative in AI product gross margins.74% similarUnverifiedThe true cost reductions come from post-training optimization, request routing, and capacity terms negotiated down based on usage volume; the focus should be on the cost per completed task, not the token unit price.72% similarUnverifiedTask-domain-specific training can reduce inference compute resources in exchange for a moderate trade-off in general-purpose capability.71% similarUnverifiedIn MobileNetV2 fine-tuning, the closer the data volume is to the original pretraining distribution and the fewer the samples, the higher the proportion that can be frozen; conversely, more layers should be released to participate in training.71% similarUnverifiedA 10% improvement in training efficiency means a cloud vendor can serve more customers with the same hardware investment, or offer lower prices for equivalent workloads.71% similar
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