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

Back to Articles NVIDIA Kumo Relational Outperforms Supervised Machine Learning on Relational Data, No Training Required Enterprise + Article Published October 21, 2026 Upvote 2 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

Details

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

Context

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 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

Impact

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 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

What's Next

These results make a compelling case for replacing production XGBoost pipelines with Kumo Relational Simplicity and agility: One pre-trained model replaces separate pipelines for individual tasks A new prediction becomes a query instead of a project, so ML teams can finally take on use cases that traditional ML made too laborious to build at every level of the organization

Background

Agents that can predict: LLMs let agents read, write, and reason Kumo Relational is already built to be called by agents, and its Predictive Query Language PQL gives them a simple interface for asking predictive questions Today, we are introducing Kumo Relational paper, documentation, a pre-trained foundation model that brings this paradigm to relational data

Analysis

Connect a database, describe what to predict, and get predictions in a single forward pass Real-world data lives in relational databases instead: users connected to orders connected to products, all evolving over time Flattening these relationships into one row through manual aggregation e.g., "average order value in the last 30 days" loses fine-grained structural signals and takes significant human effort

More

Kumo Relational is a Relational Foundation Model that operates directly on the database, respecting its tables, foreign keys, and timestamps It is pre-trained once on synthetic relational data that reflects real-world datasets and adapts to each new task through in-context learning, reading up to 10,000 context examples at inference time Each example pairs a local subgraph around an entity at a point in time with its ground-truth target e.g., "this user churned" or "this item sold 142 units"