NVIDIA Kumo Tabular Sets a New Accuracy-Efficiency Frontier for Tabular Prediction huggingface.co

NVIDIA released Kumo Tabular, an open-source foundation model for tabular data that can predict labels for new rows without training or feature engineering, available in three sizes from 28M to 215M parameters. The model was pretrained entirely on artificial data and ranks first on four benchmarks (TabArena, BeyondArena, TALENT, and ScoringBench) while running 17 times faster than competing systems. Kumo Tabular uses a Transformer architecture with specialized attention mechanisms for columns, rows, and in-context learning to handle classification and regression tasks on structured data.