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We present unit scaling, a paradigm for designing deep learning models thatsimplifies the use of low-precision number formats. Training in FP16 or therecently proposed FP8 formats offers substantial efficiency gains, but can lacksufficient range for out-of-the-box training. Unit scaling addresses this byintroducing a principled approach to model numerics: seeking unit variance ofall weights, activations and gradients at initialisation. Unlike alternativemethods, this approach neither requires multiple training runs to find asuitable scale nor has significant computational overhead. We demonstrate theefficacy of unit scaling across a range of models and optimisers. We furthershow that existing models can be adapted to be unit-scaled, training BERT-Largein FP16 and then FP8 with no degradation in accuracy.

Unit Scaling: Out-of-the-Box Low-Precision Training