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Comment by j_shi | Hacker News Reader

Self-hosted + self-trained LLMs are probably the future for enterprise.

While consumers are happy to get their data mined to avoid paying, businesses are the opposite: willing to pay a lot to avoid feeding data to MSFT/GOOG/META.

They may give assurances on data protection (even here GitHub copilot TOS has sketchy language around saving down derived data), but can’t get around fundamental problem that their products need user interactions to work well.

So it seems with BigTechLLM there’s inherent tension between product competitiveness and data privacy, which makes them incompatible with enterprise.

Biz ideas along these lines: - Help enterprises set up, train, maintain own customized LLMs - Security, compliance, monitoring tools - Help AI startups get compliant with enterprise security - Fine tuning service

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In the book “To sleep in a sea of stars” there’s a concept of a “ship mind” that is local to each space craft. It’s smarter than “pseudo ai” and can have real conversations, answer complex questions, and even tell jokes.

I can see a self-hosted LLM being akin to a company’s ship-mind. Anyone can ask questions, order analyses, etc, so long as you are a member of the company. No two LLM’s will be exactly the same - and that’s ok.

https://fractalverse.net/explore-to-sleep-in-a-sea-of-stars/...

I suspect the major cloud providers will also each offer their own “enterprise friendly” LLM services (Azure already offers a version of OpenAI’s API). If they have the right data guarantees, that’ll probably be sufficient for companies that are already using their IaaS offerings.

Enterprises should work on an open source LLM and run it on their own. This also helps people like you and me to run LLM at home.

It has worked before like in case of Linux and can work again.

Powerful LLMs are so large that they can only be trained by the major AI companies. Even LLaMA 65B (where the open release was less than intended) can't compete with GPT-3.5, let alone GPT-4. And the price for the most powerful models will only increase now, as we have effectively an arms race between OpenAI/Microsoft and Google. Few, if anyone, will be able to keep up.

Linux is different. It doesn't require huge investments in server farms.

I think you would be interested in Google's internal memo[0] that did the rounds here a couple weeks ago. The claim is that OpenAI and all competition is destined to fall behind open-source. All you need is a big model to be released and all fine tuning can be done by a smart, budget, distributed workforce.

[0]: https://www.semianalysis.com/p/google-we-have-no-moat-and-ne...

But why would a big model be released? LLaMA can't even begin to compete with GPT-4. Fine-tuning won't make it more intelligent. The only entity currently able to compete with OpenAI/Microsoft is Google with their planned Gemini model.

…today. But with the amount of (justifiable, IMO) attention LLMs are now getting, I don't see how this won't change soon. And there's quite a bit of incentive for second- or third-tier companies to contribute to something that could kneecap the bigger players.

How do the data rights broadly differ between OpenAI API directly and through Azure's endpoint?

I don’t think they do. From what I can see, Azure OpenAI is just a forwarder to the OpenAI instance.

The big benefits are AAD auth and the ability to put a proxy (APIM, etc.) on the OpenAI endpoint to do quality control, metering, logging, moderation, etc. all within Azure.

> willing to pay a lot to avoid feeding data to MSFT/GOOG/META.

Right now, you can't pay a lot and get a local LLM with similar performance to GPT-4.

Anything you can run on-site isn't really even close in terms of performance.

The ability to finetune to your workplaces terminology and document set is certainly a benefit, but for many usecases that doesn't outweigh the performance difference.

Open source with own fine tuning closing in fast:

https://lmsys.org/blog/2023-03-30-vicuna/

https://www.semianalysis.com/p/google-we-have-no-moat-and-ne...

“* According to a fun and non-scientific evaluation with GPT-4. Further rigorous evaluation is needed.”

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