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NVIDIA's Kumo Tabular introduces a family of open-source foundation models designed to perform classification and regression on structured data without requiring training or feature engineering. By leveraging in-context learning within the SDM library, these models predict new rows in a single forward pass while providing uncertainty estimates for both point predictions and quantiles. This advancement lowers the barrier to deploying high-performance tabular AI by eliminating hyperparameter tuning and supporting commercial use under permissive licenses.

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NVIDIA Releases Kumo Tabular: Open Tabular Foundation Models That Predict New Rows in a Single Forward Pass

MarkTechPostOctober 1, 20264 min read

NVIDIA has released Kumo Tabular, a new family of tabular foundation models (TFMs) for classification and regression. If you have followed TabPFN or TabICL, the setup will look familiar. The model takes labeled rows as context and predicts new rows in one forward pass. There is no training, no hyperparameter tuning, and no feature engineering. […] The post NVIDIA Releases Kumo Tabular: Open Tabular Foundation Models That Predict New Rows in a Single Forward Pass appeared first on MarkTechPost.

NVIDIA has released Kumo Tabular, a new family of tabular foundation models (TFMs) for classification and regression. If you have followed TabPFN or TabICL, the setup will look familiar. The model takes labeled rows as context and predicts new rows in one forward pass. There is no training, no hyperparameter tuning, and no feature engineering. Kumo Tabular comes in Small, Medium, and Large versions, spanning about 28M to 215M parameters. It runs through NVIDIA’s…

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