LocalLLaMA

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Community to discuss about Llama, the family of large language models created by Meta AI.

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Should corporations and smaller businesses be training, refining, and running local LLMs to avoid the future cost of the new cloud services M$ and Amazon are rolling out? (alien.top)

submitted 9 months ago by 3-4pm@alien.top to c/localllama@poweruser.forum

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It seems to me that the next big boom for cloud computing will be offering to train and host models that understand the unique business domains it serves.

Are the smart corporations already training local LLMs to understand and answer questions about their business, or is this space too new to accommodate them?

I feel like some of you may be missing a huge business opportunity. You may not realize the value of what you have already researched.

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[–] 3-4pm@alien.top 1 points 9 months ago (1 children)

why would I spend a few million dollars and a year of project time to build out local infrastructure that'll already be outdated by the time it's installed

That's probably the argument for all cloud architecture.

Long-term cost and risk might be persuasive, but that hasn't swayed IT managers thus far for non-LLM specific infrastructure. I am guessing it won't do much to sway future IT managers.

I'm also assuming Azure will let you get very custom with the LLMs you can train via their services.

This gives me something to think about.

[–] FlishFlashman@alien.top 1 points 9 months ago (1 children)

That's probably the argument for all cloud architecture.

Long-term cost and risk might be persuasive, that hasn't swayed IT managers thus far for non-LLM specific infrastructure

It's 2023. What are you talking about? Where have you been?

[–] _-inside-_@alien.top 1 points 9 months ago

Not everyone still uses the cloud, I still know people who run and manage physical clusters. This is mostly true for institutions such as hospitals, universities, etc. Using cloud solutions on these cases not just would add external dependencies but also much higher costs, for instance, handling and processing hundreds or thousands of terabytes of critical or scientific data.