Tabular data

TabFM

Google's large tabular model: classification and regression from examples without training, with numeric and categorical columns. Weights are for non-commercial use only.

Developer
Google Research, USA
First release
Jun 2026
Latest release
Jun 2026
Sizes
about 1.6B
License
Non-commercial onlyNon-commercial TabFM license
Running
On your own serverNeeds a GPU
Industries
Science and research, Finance
Deploy on your server Hugging Face GitHub

What it does

  • Pilot comparison with current scoring models
  • Exploring customer data
  • Testing churn hypotheses

Where it is used

R&D and analyticsFinanceSales and CRM

Hardware requirements

LaptopLaptop or regular PC, up to 8 GB of VRAM — smaller versions
no versions
1 GPUOne GPU with 16–80 GB — mid-size versions
fits
ClusterServer with several GPUs — flagship versions
no versions

Versions

  1. TabFM 1.0.0

How to run it

On your own serverWeights are downloaded from Hugging Face and served with vLLM (load and API) or llama.cpp (modest hardware). The model then runs in a closed network with no per-request fees.Hugging Face
How much hardware you needCalculate the VRAM for the model size, context length and number of concurrent requests.Open the hardware calculator

I can set this up end to end: pick the model size, deploy it on your server and connect it to your systems.

Frequently asked questions

Can TabFM be used in a commercial project?

No. License: Non-commercial TabFM license. A commercial product needs a different model or a separate agreement with the rights holder.

What hardware does TabFM need?

At minimum: One GPU with 16–80 GB — mid-size versions. Without a GPU the model is not practical. You can calculate the exact VRAM for your model size and context in the hardware calculator.

Does TabFM support Russian?

Language does not matter for this model: it does not work with text.

Where can I download TabFM and what does it cost?

The TabFM weights are open and free to download. You only pay for the hardware it runs on and for the setup. Source links are at the bottom of this page.

How I deploy it for clients

  1. SelectionI pick the model size for your task and hardware and test it on your examples.
  2. DeploymentI deploy it on your server or in a closed network and provide an API.
  3. Fine-tuningI fine-tune it on your data (LoRA) or connect a knowledge base — whichever is cheaper for the task.
  4. IntegrationI connect it to your CRM, ERP, bot, website or team chat and set up monitoring.

Similar models

Tabular dataTabPFNPrior Labs (University of Freiburg) · GermanyCommercial use with conditions

A ready-made model for tables: it takes example rows and immediately predicts for new ones, without lengthy training or tuning. Only v2 is free for business; newer versions are non-commercial.

DetailsTabular dataTabICLInria (Soda team) · FranceCommercial use allowed

An open tabular model from the creators of scikit-learn: classifies and predicts from examples without training and handles tables of up to hundreds of thousands of rows. The license allows business use.

DetailsTabular dataEXAONE TabularLG AI Research · South KoreaNon-commercial only

LG's small tabular model: with 21M parameters it nearly matches the leaders in classification and regression accuracy. Weights are for non-commercial use only.

Details

Source: huggingface.co/google/tabfm-1.0.0-pytorch. Data checked against the model card on 22 Sep 2026. Have a lawyer review the license before commercial launch.