Martin Seeger on Nostr: Now to inference: using a model after it has been trained. I want to separate the ...

Now to inference: using a model after it has been trained. I want to separate the technology from its application, because the discussion is already carrying enough luggage.

Technically, two delivery modes matter to me.

With **AI as a service**, a provider operates the model and sells access. This offers convenience, rapid updates, elastic capacity, and often the strongest available capabilities. It also means less control and continuing dependency on prices, policies, and availability.

With **open-weight AI**, the learned parameters can be downloaded and run or adapted elsewhere, including locally. That can offer privacy, control, and portability. Open weights are not necessarily open source, though. Training data, training code, or broad usage rights may still be missing.

Neither approach is inherently right. Sadly, the universe has declined to provide one architecture that solves every trade-off.

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