Tabular foundation models aim to replace LLMs on structured data
bendee983 · x · 2026-07-28
A thread argues that tabular and relational foundation models are emerging as the right fit for structured data, where standard LLM-style in-context learning breaks down.
- The core claim is that tables are not prose: they are two-dimensional, row-order-free, and need models that treat the table itself as the prompt.
- These models can do classification and regression in one forward pass, often zero-shot, without per-dataset training.
- The post cites Google TabFM, Nvidia KumoRFM, TabPFN, and TabICL as examples.
- It highlights scale claims such as support for up to 500 features, 500,000 samples, and up to 10x speedups on large datasets versus prior generations.
- Warehouse integration is mentioned as part of the roadmap for the open-source Google path.
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