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

> **Publication**: aitalky (Independent AI Journalism)
> **Canonical Citation URL**: https://aitalky.vercel.app/news/nvidia-kumo-relational-outperforms-supervised-machine-learning-on-relational-data-no
> **Original Source**: [Hugging Face](https://huggingface.co/blog/nvidia/kumo-relational)
> **Author**: Hugging Face Staff (undefined)
> **Published Date**: Wednesday, October 21, 2026
> **Category**: PRODUCTS
> **Tags**: AI, Hugging Face, products

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



## Full Coverage
Back to Articles a]:hidden"> NVIDIA Kumo Relational Outperforms Supervised Machine Learning on Relational Data, No Training Required Enterprise + Article Published October 21, 2026 Upvote 7 +1 Joshua Przybylko jprzybylko Follow nvidia Jure Leskovec profjure Follow nvidia Matthias Fey rusty1s Follow nvidia Federico Lopez flopeznv Follow nvidia Valter Hudovernik valterh-nv Follow nvidia Akihiro Nitta akihironitta-nv Follow nvidia Ramona Bendias RBendiasN Follow nvidia Ardrian Wong aw777 Follow nvidia :last-child]:mb-0"> TL;DR The Shift to Structured Data Foundation Models How Kumo Relational Works How Kumo Relational Was Built Performance Limitations Demo Start Building with NVIDIA Kumo Relational Acknowledgements 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

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:

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