The non-finetuned BLOOM does not appear favorably (in English) compared to GLM or OPT, which both have published weights: https://crfm.stanford.edu/helm/v0.1.0/?group=mmlu and Flan-T5 is above OPT-IML: https://arxiv.org/pdf/2212.12017.pdf
> Is the future going to be controlled by big corporations who own the models themselves?
On this subject, there is an effort stemming from BigScience to build an open, distributed inference network, so that people that don’t have enough GPUs at home can contribute theirs and get text generation at one word per second: https://github.com/bigscience-workshop/petals#how-does-it-wo...
In practice these models are typically run using top-tier A100 GPUs, which apparently is the cheapest thing you can do at scale: https://forum.effectivealtruism.org/posts/foptmf8C25TzJuit6/.... It looks like you can get away with just $10/hour, but I'm not sure I believe it. In one hour you can roughly generate 6 million English words this way, that's quite cheap.
But if you want to own the full hardware, then it's quite more expensive. You need 8 of those A100 GPUs, which come at $32k a piece, so you're in the ballpark of > $300k to build the server you need. Then there's of course running costs, these GPUs burn 250W a piece, plus the rest of the server we're at about 3kW power. That's not much, maybe $0.50/hr, plus maybe another $1/hr to cool the room it's in, depending on where it is (and the season, I guess in winter a fan might suffice, it's about as powerful as a couple small electric heaters). So with an upfront expense of > $300k, you're maybe down from $10/hr to $1.5/hr, saving something like $8.5/hr, which is $6k / month (minus the rent of whatever place you put the server in).
All in all, it's definitely feasible for a small start up as well, but not very much for an individual.