NVIDIA Kumo Relational is a pre-trained relational foundation model that makes predictions directly on enterprise databases. Given a predictive query, it gathers up to 10,000 historical examples as context and predicts outcomes such as churn, demand, or fraud in a single forward pass. It needs no task-specific training, no feature engineering, and no flattening of tables. Kumo Relational is the first few-shot foundation model to outperform supervised machine learning on common relational benchmarks. Across 41 tasks on 15 databases, it beats supervised relational deep learning models and expert-engineered gradient-boosted tree pipelines, and on the SAP SALT enterprise benchmark it surpasses AutoGluon ensembles by 8% in mean reciprocal rank (MRR). It scales to databases with 500B+ rows and works for humans and agents alike, through NVIDIA's open-source, GPU-native structured-data-models library or as an NVIDIA NIM.

Foundation models have already rewritten the playbook for language and vision. Not long ago, production NLP meant a dedicated model per task, each fed by its own pipeline of tokenization, lemmatization, and n-grams. LLMs made that stack largely obsolete for three reasons: they were more accurate than the best task-specific models, far simpler to put into production, and flexible enough to open a new era of agentic applications.

The same shift is now underway for structured data. Relational databases power the world's most critical business decisions, yet predictive modeling on them still requires weeks of feature engineering, task-specific training, and tuned pipelines. Structured data foundation models change that for the same three reasons:

Better accuracy today: As drop-in replacements, these models already beat many production pipelines, especially XGBoost.

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