You can always self host an LLM which is completely controlled on your own server. This is trivial to do.
Yup. Trivial.
But also, running LLMs locally is easy. I don't know what goes into hosting them, as a service for your org, but just getting an LLM running locally is a straightforward 30-minute task.
Say oai implements something that makes their service 2x better. Just using it for a while should give people who live and breathe this stuff enough information to tease out how to implement something like it, and eventually it'll make it into the local-only applications, and models.
https://getdeploying.com/guides/run-deepseek-r1 this is the "how to do it"
https://news.ycombinator.com/item?id=42897205 posted here, a link to how to set it up on an AMD Epyc machine, ~$2000. IIRC a few of the comments discuss how many GPUs you'd need (a lot of the 80GB GPUs, 12-16 i think), plus the mainboards and PSUs and things. however to just run the largest deepseek you merely need memory to hold the model and the context, plus ~10% and i forget why +10% but that's my hedge to be more accurate.
note: i have not checked if LM Studio can run the large deepseek model; i can't fathom a reason it couldn't, at least on the Epyc CPU only build.
note too: I just asked in their discord and it appears "any GGUF model will load if you have the memory for it" - "GGUF" is like the format the model is in. Someone will take whatever format mistral or facebook or whoever publishes and convert it to GGUF format, and from there, someone will start to quantize the models into smaller files (with less ability) as GGUF.
If i can find it later, as i couldn't find it last night when i replied, there is an article that explains how to start adding consumer GPUs or even 1-2 Nvidia A100 80GB GPUs to the epyc build, to speed that up. I have a vague recollection that can get you up to 20t/s or thereabouts, but don't quote me on that, it's been a while.