As I understand it, all real world use cases requires provider data centers and the local efforts are experimental.
So "open models" buy you little and the independence from the oligopoly is not achieved.
So "open models" buy you little and the independence from the oligopoly is not achieved.
Still, even if you need other people's industrial-class hardware, open models offer a lot more freedom and options. They allow you to use GPU capacity from entities who are not themselves building or training models and are ostensibly disinterested.
There's a big range of possibilities here in terms of data sovereignty and so forth.
1) You can use OpenRouter to route your open model requests to US-based inference providers with ZDR (zero data retention), as far as you can believe anything in this world. If you look at who actually serves open Chinese models on OpenRouter, you'll see a lot of folks like Digital Ocean, etc. I suppose I can't vouch for their purity, no, but I'd much rather send data there than send it to Dario.
2) Or, you can rent GPUs from companies like Runpod or Vast.ai and serve some very sizable models to yourself (e.g. using their pre-built vLLM images). You can't serve a model like Kimi K3 to yourself that way, at least not in any economically reasonable way. However, a private H100SXM or B200 can go a long way. You could serve the big Qwens, or DeepSeek-V4-Flash--you could do a lot if you're willing to spend on a rented GPU with sizable VRAM.
3) Finally, if you have and want to spend $750K-$1MM+ (I suspect I'm low-balling at this point), and if you can get them in the current demand climate, you can absolutely buy 16 x H200SXMs, with the appropriate boards to take them, pay for 20-25 kW of cooling, etc., and run one of these models yourself, on your kitchen floor if you like. You simply cannot do that with Anthropic or OpenAI.