There are quite a few. The best ones are sustainable closed loop datacenters with on-site solar which is becoming pretty common across the world, especially for new builds. Often producing more power than they need and feeding it back to the grid (especially if the local government has an energy buy back scheme).
But most data centers are pretty tiny and just built into an office building with a bunch of server racks.
Depending on where you live, a quick web search for data centers in your local area will probably show up dozens of them of varying quality hosting people's websites and business apps etc. They aren't any scarier than anything else you find in a city. They're critical infrastructure that helps make the internet a thing. In most cases, if it wasn't a datacenter, it would be a car yard or a factory, etc.
But! There are also truly evil datacenters. Like this insane Utah monstrosity built for a shitty purpose and the size of a freaking city. An obscene monument to the US tech cesspool's hubris.
While this advice is true for all models, when it comes to agentic tasks (add this small feature/write this test harness/find bugs/suggest improvements), open source models are still way behind, vibe code or not.
Claude Fable or even Opus in an editor like Zed have a 1 million token context window and will "think" through the goals of the application, test their changes, work through debugging processes the way a programmer would, stop to ask for clarification, check diagnostic tools and linters, prompt to run test code, etc.
Llama, Gemma and Qwen etc. Do lack a lot of the world knowledge to get the goals of the application, but they also just don't have the debugging skills, won't test their code, don't always tool call correctly, get confused as the context increases and nobody has enough vram to run on large context sizes locally.
They can do autocomplete on small functions but aren't really there for more complex tasks.
On top of that, the biggest problem is that the best open source models are trained and released by the same giant tech conglomerates that have an interest in not competing with their own products. Qwen is Alibaba, Llama is Meta, gpt-oss is OpenAI. Even the more "independent" ones, kimi (Moonshot) and GLM (z.ai) are mostly funded by Alibaba and Tencent. They're released for research and marketing purposes and to please their corporate backers with inflated stock. Almost nobody has the resources to train new models from scratch. People make lots of merges and fine tunes but AI is not democratised the way that traditional programming tools have been.
Maybe some day there will be enough cheap compute for open source communities to pool together resources to build competing models but they're not really there yet :(