NVIDIA Kumo Relational Outperforms Supervised Machine Learning on Relational Data, No Training Required

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 historical examples as context examples 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 and kumo-engine libraries, or as an NVIDIA NIM. Paper: https://arxiv.org/abs/2604.12596 Model Code: https://github.com/NVIDIA/structured-data-models Model Weights: https://huggingface.co/nvidia/Kumo-Relational Client Code: https://github.com/NVIDIA/structured-data-models-client NIM 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. 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. On RelBench V1 classification tasks, Kumo Relational outperforms gradient-boosted trees on expert-engineered features by nearly 4 AUROC points on average. On SAP SALT, it beats XGBoost on engineered features by 7 MRR points out of the box and by 13 MRR points after fine-tuning. These results make a compelling case for replacing …