I don’t really get the business plan for open weights model companies, is the idea companies would pay them for serving?

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tfehring a day ago | parent | next [-]

Thinky's main commercial product AFAIK is Tinker [0] - companies pay them to host their fine-tuning workloads and then the resulting fine-tuned models. I don't know if this is a good business plan, but I'm sure at least one person there has read Joel on Software [1].

[0] https://thinkingmachines.ai/tinker/

[1] https://www.joelonsoftware.com/2002/06/12/strategy-letter-v/

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mchusma 19 hours ago | parent | next [-]

I don’t know if it’s a great business model but it makes perfect sense to me. Open models when fine tuned are capable at better than frontier performance at a fraction of the price for many (probably most) domain specific tasks. If companies help make that easy to implement, there is value to capture. But I kind of like Unsloths model here which is to be really good at just layer, and not bothering with building their own models.

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sgt101 17 hours ago | parent | prev [-]

I don't get this - I can do LORA on my mac... ok I can't do LORA on a 1tn param model, but if I was in the tn parameter model game I would get some kit that I could use to do that...

What's their moat / secret sauce?

	▲	tfehring 7 hours ago \| parent [-]
		Like, buy and set up the physical hardware? I cba with that. Plus the hardware you want for LoRA (the type but especially the quantity) is different than what you want for inference, so either you'd under-spec it and wait forever for fine-tuning runs, or over-spec it and have low utilization most of the time. And even then who knows if it would be good enough to LoRA next year's best open source model. AWS gets great margins for renting out commodity hardware as a service because it built the right abstractions and can serve them efficiently at scale, I think the arguments here are basically the same.

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janalsncm a day ago | parent | prev | next [-]

From what I can tell it’s a bit of the following.

1. Magic

2. Managed hosting of their model

3. Hurting competitors. If people are using Meta’s commoditized models they’re not paying Google or allowing OpenAI to become too big.

4. Free R&D from open source. If open source developers are optimizing systems to run Llama, that helps Meta.

5. More magic

	▲	sgt101 17 hours ago \| parent [-]
		I think that LeCunns belief was/is that LLM's have limited value and are a dead end, so what they wanted to do was kill any competitor while evolving tech that was/is a winner. Well, he lost his job on that bet... and yet... I do not think that the verdict of history is quite in.

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JumpCrisscross a day ago | parent | prev | next [-]

> don’t really get the business plan for open weights model companies

Feature as a company for now. Apple is struggling to build an in-house model set. And plenty of software behemoths, e.g. IBM, are realising they don't have a ticket to the new tech economy.

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ignoramous a day ago | parent | prev [-]

> don't really get the business plan for open weights model companies

The Chinese "Neijuan" aside, most competing labs are going for the classic, 'your margin is my opportunity': https://tomtunguz.com/is-your-margin-my-opportunity-software... / https://archive.vn/5Vmq3