Verified75% confidenceTradeoffExact time
Distillation allows open-source teams to achieve performance close to frontier models at a cost far lower than training from scratch.
3
Sources
75%
Confidence
Long-term
Relevance
9/12/2026
First Seen
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Related Claims
UnverifiedWhen facing new domain tasks, one should first try running baselines with off-the-shelf open-source models, as usable results can often be obtained at very low cost and it is quite likely that training one's own model will not be necessary.76% similarUnverifiedSmall models obtained through distillation can be deployed locally, run offline, and used long-term after a single training run, with marginal cost approaching zero.74% similarUnverifiedSince 2024, teams such as DeepSeek have demonstrated that through more sophisticated training strategies, performance levels close to those of leading models can be achieved with a far lower compute budget.73% similarVerifiedFine-tuning typically requires only one percent or less of the original training cost to achieve domain performance close to that of a dedicated model.73% similarUnverifiedThe safety alignment in the original training of open-source models can be removed through a small amount of fine-tuning.73% similar
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