Tabular data

TabDPT

A tabular model from a Canadian bank's AI lab, trained on real tables rather than only synthetic ones. Version 1.2 Turbo made computation orders of magnitude faster.

Developer
Layer 6 AI (TD Bank), Canada
First release
Mar 2025
Latest release
Sep 2026
Sizes
about 60–80M
License
Commercial use allowedApache 2.0
Running
On your own serverAlso runs without a GPU
Industries
Finance
Deploy on your server Hugging Face GitHub

What it does

  • Scoring applications and customers
  • Predicting churn
  • Classifying transactions and customers

Where it is used

Banking and financeInsuranceSales and CRM

Hardware requirements

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

Versions

  1. TabDPT 1.3
  2. TabDPT 1.2 (Turbo)
  3. TabDPT 1.1
  4. TabDPT 1.0

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 TabDPT be used in a commercial project?

Yes. License: Apache 2.0. It allows commercial use, but it is still worth having a lawyer review the license before launch.

What hardware does TabDPT need?

At minimum: Laptop or regular PC, up to 8 GB of VRAM — smaller versions. Some versions also run on an ordinary CPU, without a GPU. You can calculate the exact VRAM for your model size and context in the hardware calculator.

Does TabDPT support Russian?

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

Where can I download TabDPT and what does it cost?

The TabDPT 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 dataMitraAmazon (AutoGluon team) · USACommercial use allowed

Amazon's tabular model built into AutoGluon: classification and regression from examples with brief fine-tuning. Mitra-v2 handles more rows and columns.

Details

Source: github.com/layer6ai-labs/TabDPT-inference/releases. Data checked against the model card on 22 Sep 2026. Have a lawyer review the license before commercial launch.