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The scaling of Transformers has driven breakthrough capabilities for languagemodels. At present, the largest large language models (LLMs) contain upwards of100B parameters. Vision Transformers (ViT) have introduced the samearchitecture to image and video modelling, but these have not yet beensuccessfully scaled to nearly the same degree; the largest dense ViT contains4B parameters (Chen et al., 2022). We present a recipe for highly efficient andstable training of a 22B-parameter ViT (ViT-22B) and perform a wide variety ofexperiments on the resulting model. When evaluated on downstream tasks (oftenwith a lightweight linear model on frozen features), ViT-22B demonstratesincreasing performance with scale. We further observe other interestingbenefits of scale, including an improved tradeoff between fairness andperformance, state-of-the-art alignment to human visual perception in terms ofshape/texture bias, and improved robustness. ViT-22B demonstrates the potentialfor "LLM-like" scaling in vision, and provides key steps towards getting there.

Scaling Vision Transformers to 22 Billion Parameters