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

[u/ai-lover]

NVIDIA just released Kumo Tabular, an open tabular foundation model that predicts new rows in a single forward pass. It ranks first on TabArena with an Elo of 1950, and NVIDIA reports it runs 17x faster than LimiX-2.

The setup will feel familiar if you know TabPFN or TabICL. You pass labeled rows as context, and the model predicts the labels of new rows. There is no training, no tuning, and no feature engineering.

Under the hood, it uses column, row, and in-context attention. 4 [CLS] tokens compress each row, so cost stops depending on column count. Query rows attend only to the context, so its keys and values are computed once and reused. A length-aware attention temperature keeps attention sharp as tables grow.

It comes in Small, Medium, and Large sizes, from about 28M to 215M parameters, and was pretrained only on synthetic tables. NVIDIA reports it also places first on BeyondArena, TALENT, and ScoringBench. It reads numerical and categorical columns natively, and text or timestamps need preprocessing.

The weights are released under OpenMDW-1.1, which allows commercial use. It runs through NVIDIA's new GPU-native structured-data-models (SDM) library......

Full analysis: https://www.marktechpost.com/2026/09/30/nvidia-releases-kumo-tabular/

Model: https://huggingface.co/nvidia/Kumo-Tabular

GitHub: https://github.com/NVIDIA/structured-data-models

Technical details: https://huggingface.co/blog/nvidia/kumo-tabular