What limits LLM inference accelerators? I heard about Groq (https://groq.com/) not sure how much it pushes away the problem.
Also - turbine blades limit power, according to Elon.
Between them - we cannot chip fabs past a certain rate, and we cannot stand up the datacenter to run these desired chips past a certain rate. Different people believe one or the other is the 'true' current bottleneck. The turbine supply chain scaling looks much more tractable -- EUV is essentially the most complicated production process humans have ever devised.
Amount of power: World solar power generation capacity is in the terawatts and rapidly increasing, there's no issue with its potential ceiling. As a bonus, it tends to work best on land that's useless for other purposes.
- clean room, itself needing the infrastructure for it (size, airCo, filtering, electricity) and the staff to run and maintain that basically empty space - wafers to "print" on, so that's a lot of water and logistic to manipulate them (so infrastructure for clean water and all chemicals) also with dedicated staff - finally staff who would be able to design something significantly better than NVIDIA, Intel, Broadcom, IBM, etc while (and arguably that's the trickiest part IMHO) being able to get it good enough as at a scale that can be manufactured from their own fab.
so I'm wondering who can afford this kind of setup that can only then make use of ASML machines.
Fabs are some of the most complex chemical engineering sites (dealing with some of the most dangerous substances) in the world. So don't underestimate the complexity of this part.