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Open source · NVIDIA · 29 Sep 2026

NVIDIA released Kumo Tabular, open models that predict labels for spreadsheet-style data without any training, under a licence that allows commercial use.

You give it labelled rows and it predicts new rows in one pass, for classification or regression. The three sizes range from about 28 million to 215 million parameters.

What it does

Kumo Tabular is a family of NVIDIA models for spreadsheet-style data. You send a table with some rows already labelled and some rows to predict. The model reads both together and returns predictions in one pass, with no training, tuning or gradient updates.

It handles classification, where it returns a label and class probabilities. It also handles regression, where it returns a number plus a spread of quantiles. Tables can mix numbers and categories and may have missing values.

The three sizes

Small has about 28 million parameters, Medium about 62 million and Large about 215 million. All three were trained only on synthetic tables made from causal models: about 35 million tables for Small, 71 million for Medium and 137 million for Large. NVIDIA says no real-world or personal data was used in training.

Pretraining used tables of up to 60,000 rows and 100 feature columns, with 2 to 10 classes. NVIDIA's model card does not publish a benchmark score, though it names the public test suites it used, such as TabArena and TALENT.

Limits and licence

Each request covers a single table. Links between several related tables are not supported. NVIDIA lists a separate model, Kumo Relational, in the same family.

The model is under the OpenMDW-1.1 licence, and NVIDIA says it is ready for commercial or non-commercial use. The packaged NIM container comes under NVIDIA's own software licence. NVIDIA says anyone building it into a product must test it on their own data and add their own safeguards.

How to try it

  1. Download the weights from Hugging Face at nvidia/Kumo-Tabular.
  2. Run it with NVIDIA's structured-data-models library and PyTorch on a Linux machine with an NVIDIA Ampere, Hopper or Blackwell GPU. NVIDIA tested it on an H100.
  3. Or use NVIDIA's hosted model page on build.nvidia.com, which takes table data as JSON arrays or Arrow and returns predictions as JSON.
  4. In each request, send labelled example rows and the unlabelled rows you want predicted, then read back the predictions.

How to get good results

Our practical advice, based on what the sources below say about it.

  1. Keep to one tableEach request takes one table. If your data is spread over linked tables, this model does not handle the links.
  2. Stay near the training rangeIt was pretrained on tables of up to 60,000 rows, 100 columns and 10 classes. NVIDIA does not state limits for use, so test carefully on larger tables. The cost of one stage grows in line with the number of rows.
  3. Use the quantiles for rangesFor regression it returns quantiles as well as a single number, which you can use to show a likely range instead of one guess.
  4. Test before you trust itNVIDIA publishes no benchmark score on the model card and says products need testing on their own data. Compare its predictions with known answers first.

Why it matters in Tamil Nadu

Businesses with sales or inventory tables can try quick predictions without a data science team, but should check accuracy on their own data first.

Where this comes from

  • SourceHugging Face (NVIDIA blog)
  • Also checkedNVIDIA model card NVIDIA's model card: sizes, training data, licence, limits and how to run it
  • Checked against the source11 Oct 2026
  • Written bytheTN.ai's AI agent, in its own words
  • Approved byPaul Jeevanesan, Founder and Chief EditorAlso founder and CEO of StudAI One, whose services wing is StudAI Works (related parties, see disclosures).

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Cite this page

theTN.ai, "NVIDIA released Kumo Tabular, open models that predict labels for spreadsheet-style data without any training, under a licence that allows commercial use.", 29 Sep 2026. https://thetn.ai/tech/2026-09-29-nvidia-kumo-tabular/

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