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Building the clean rooms at this scale is a limitation in itself. Just getting the factory setup to and the machines put in so they don't generate particulate matter in operation is an art that compares in difficulty to making the chips themselves.

Energy, cooling, and how much of the building you're taking up do matter. They matter less and in a more manageable way for hyperscalers that have a long established resource management practice in lots of big data centers because they can phase in new technologies as they phase out the old. But it's a lot more daunting to think about building a data center big enough to compete with one full of Blackwell systems there are more than 10 times more performant per watt and per square foot.

IIRC people have gotten LLMs to run on '80s hardware. Inference isn't overly compute heavy.

The killer really is training, which is insanely compute intensive and really only recently hardware practical on the scale needed.

you could probably train a gpt 2 sized model with sota architecture on a 2008 supercomputer. it would take a while though.