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It’s probably a stretch to run GPT-3 inside a db, but most of the “deep learning” models I’ve run in more traditional environments are a few megabytes. That’s millions of params, but Morre’s law has been generous enough to us over the decades that I think there is a good case to spend a few megabytes of DB ram on ML. I would think this idea has really landed though, when we start hearing about Postgres deployments with GPUs on board though :)

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You can already do that by using the pl/python or pl/java extensions with the right environment. However, the interface between SQL and model inference is typically narrow enough that IMO it's better to: read from Postgres --> process in an external Python/Java process --> persist to Postgres.

Maybe enhance this with a FDW to an external inference process to allow triggering of inference from Postgresql itself.

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