1. Organizations that care about controlling their data. Pretty much the same ones that were reluctant to embrace the cloud and kept their own server rooms.
An additional flavor to that: even if my professional AI agent license guarantees that my data won't be used to train generic models, etc., when a US court would make OpenAI reveal your data, they will, no matter where it is physically stored. That's kinda a loophole in law-making, as e.g., the EU increasingly requires data to be stored locally.
However, if one really wants control over the data, they might prefer to run everything in a local setup. Which is going to be way more complicated and expensive.
2. Small Language Models (SLMs). LLMs are generic. That's their whole point. No LLM-based solution needs all LLM's capabilities. And yet training and using the model, because of its sheer size, is expensive.
In the long run, it may be more viable to deploy and train one's own, much smaller model operating only on very specific training data. The tradeoff here is that you get a cheaper in use and more specialized tool, at the cost of up-front development and no easy way of upgrading a model when a new wave of LLMs is deployed.
An additional flavor to that: even if my professional AI agent license guarantees that my data won't be used to train generic models, etc., when a US court would make OpenAI reveal your data, they will, no matter where it is physically stored. That's kinda a loophole in law-making, as e.g., the EU increasingly requires data to be stored locally.
However, if one really wants control over the data, they might prefer to run everything in a local setup. Which is going to be way more complicated and expensive.
2. Small Language Models (SLMs). LLMs are generic. That's their whole point. No LLM-based solution needs all LLM's capabilities. And yet training and using the model, because of its sheer size, is expensive.
In the long run, it may be more viable to deploy and train one's own, much smaller model operating only on very specific training data. The tradeoff here is that you get a cheaper in use and more specialized tool, at the cost of up-front development and no easy way of upgrading a model when a new wave of LLMs is deployed.