Tabular data is finally getting its own era of ready-to-use foundation models, the way images and text already have. NVIDIA just open-sourced a new library for it. Title: Structured Data Models (NVIDIA/structured-data-models) URL: 📝 Overview A GPU-native, PyTorch-compatible library bundling foundation models, tensor types, and data processors for tabular and relational data. ❓ Problem Solved Structured data lagged behind images and text when it comes to GPU-native infrastructure, often forcing teams to retrain task-specific models from scratch. 💡 Method & Approach It ships tabular models like TabICLv2, KumoTabular, and TabFM alongside KumoRelational for graph-structured data. The core `TableTensor` abstraction losslessly tensorizes raw data on GPU, and models support both in-context learning and cached fit-predict inference. 🌍 Use Cases Numerical, categorical, datetime, text, and relational data types can all flow through pre-trained models with minimal code. It requires Python 3.11+ and PyTorch 2.7+, and installs directly via pip. 📊 Results The project claims new accuracy-efficiency frontiers for tabular prediction, plus zero-shot capability across diverse structured datasets. #MachineLearning# #GPU#