TL;DR

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.

• •Paper:https://arxiv.org/abs/2604.12596
• •Documentation:https://docs.nvidia.com/sdgm/rfm/overview

The Shift to Structured Data Foundation Models

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.