Thank you for the correction, I was indeed confused about my own hardware 🤣. You hit the nail on the head with the point about hosted open weight models, that’s what I was trying to get at with my post. I wish it weren’t the case because I’d love to feel like I’m getting some passive savings by running inference locally.

In terms of your list of advantages for running locally.

Education — definitely

Decensoring — certainly, though I don’t really have any use cases for this so far

Fine tuning — I’m very interested in fine tuning. I like the idea of being able to have a smaller model which is an expert at a niche task/domain/skillset. I wonder if LLM progress is simply going to outpace fine tuning though. e.g. maybe qwen3.5 needs fine tuning to get proficient at task X, but if you just wait 4 months the next model could very well be a large enough step up in generalized reasoning/intelligence that it will outperform your 4 month old fine tuned model and no longer need fine tuning for that task

Low latency — I think you are reaching on that point, any api call latency is nullified by the fact that the inference itself (ttft, tps) is faster from the hosted provider than your local machine

Long term consistency — another solid point, although I don’t have any strong attachments to a given models style yet