alt Hacker News> I think it's clear that inference is a viable business model.
You may be right. I'm not so sure. Inference looks like a viable business model for those operators that have SOTA infrastructure in place, but the investment required to have it is enormous, and appears to be never-ending, because if an operator stops investing aggressively, its infrastructure quickly becomes non-competitive, and customers will quickly leave for alternatives. SOTA infrastructure is a moving target.
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What about inference on a distilled version of someone else's model? What about inference on an open weights model? I don't think all LLM business models are going to revolve around developing state of the art frontier models.
Maybe... I'm not enough of an expert on the financials to say, but it seems to me that inference should be able to recoup the cost of SOTA infrastructure, unless you then also use a large portion of that infrastructure to train new models. And I also think the race to remain SOTA itself is also largely a function of training, because my understanding is that training benefits more from the leading edge of hardware.
But yeah, I definitely don't have high confidence in any of this!
Renting out compute is a viable business model. That's part of what the big and the small cloud providers do. It's just (on its own) not a "get rich quick" business model anymore.
Why would it be any different for inference? If we believe OP, it'll just become part of regular compute infra, and thus part of the renting-out-compute business model.
I think it's an open question if the current generation of inference investment will pan out, but in the long term, there'll be a balance between investment cost and margin, just as in every other industry.