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

NVIDIA Kumo Tabular, part of the NVIDIA Kumo Structured model collection, is an open foundation model for tabular data now available on Hugging Face. Given a table of labeled rows, it predicts the labels of new rows in a single forward pass, with no training, no tuning, and no feature engineering, for both classification and regression.

It was pretrained only on artificial data, comes in three sizes (28M to 215M parameters), runs through our open-source library, and is released under the OpenMDW-1.1 license for commercial use. It ranks first on the four benchmarks TabArena, BeyondArena, TALENT and ScoringBench. Tabular data is the backbone of enterprise machine learning. Customer records, transactions, sensor logs, claims, and orders all live in tables, and predicting churn, default, demand, or price from them is among the most common machine learning tasks in industry.

For two decades, this work has been done with gradient-boosted trees, and it has worked well. But the lifecycle around those models has barely changed. Every new question means collecting labels, engineering features, searching hyperparameters, validating, and deploying a model that knows…