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

- Company: NVIDIA (nvidia.com)
- Announced: 2026-10-21T18:26:30+00:00
- Category: new-model
- Coverage: not counted
- Announcement: yes
- Group: models
- Source: https://huggingface.co/blog/nvidia/kumo-relational
- Record: https://forck.live/items/26084-nvidia-kumo-relational-outperforms-supervised-machine-learning-on-relational
- Title in English (translated by forck.live from en): NVIDIA Kumo Relational Outperforms Supervised Machine Learning on Relational Data, No Training Required
- Models affected: Kumo Relational

NVIDIA introduced Kumo Relational, a pre-trained relational foundation model that predicts directly on enterprise databases without task-specific training, feature engineering or flattening of tables. 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. NVIDIA says it is the first few-shot foundation model to outperform supervised machine learning on common relational benchmarks, beating supervised relational deep learning models and expert-engineered gradient-boosted tree pipelines across 41 tasks on 15 databases. On the SAP SALT enterprise benchmark, NVIDIA says Kumo Relational surpasses AutoGluon ensembles by 8% in mean reciprocal rank, and on RelBench V1 classification tasks it outperforms gradient-boosted trees on expert-engineered features by nearly 4 AUROC points on average. The model scales to databases with 500B+ rows and is available through NVIDIA's open-source, GPU-native structured-data-models library or as an NVIDIA NIM. Tasks are written in the Predictive Query Language, a declarative interface where users state what to predict rather than how, and the model is built to be called by agents.

## Evidence

Verbatim from https://huggingface.co/blog/nvidia/kumo-relational:

> 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).

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