NVIDIA releases Kumo Tabular model for tabular prediction with open weights
NVIDIA has launched Kumo Tabular, an open-source foundation model for tabular data prediction, available on Hugging Face. The model handles classification and regression tasks without training, tuning, or feature engineering, using in-context learning on labeled tables. It comes in three sizes (28M to 215M parameters) and is pretrained exclusively on artificial data generated via procedural…
Key points
- Kumo Tabular predicts labels in a single forward pass with no training, supporting classification and regression on tabular data
- Ranks first on four benchmarks (TabArena, BeyondArena, TALENT, ScoringBench) and runs 17x faster than LimiX-2 on NVIDIA GPUs
- Pretrained on 35–137 million artificial tables, with weights open-sourced under OpenMDW-1.1 for commercial use
Kumo Tabular achieves state-of-the-art performance on four benchmarks—TabArena, BeyondArena, TALENT, and ScoringBench—while running up to 17x faster than competing models like LimiX-2 on an RTX 6000 Pro. The model supports up to 10 classes natively and includes built-in preprocessing for numerical/categorical data. NVIDIA provides an open-source library for inference, with weights hosted on Hugging Face under the OpenMDW-1.1 license, allowing commercial use. The company emphasizes validation on held-out data due to potential accuracy degradation in edge cases.
Model page: Kumo Tabular →
NVIDIA Kumo Tabular Sets a New Accuracy-Efficiency Frontier for Tabular Prediction
Hugging Face · 29 September 2026
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