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You still need the mega-compute. Even for inference, 1.5 terabytes of RAM in modern servers isn't cheap.
You can run Claude Sonnet equivalent quantised models locally on much less RAM.
Local LLMs are close to being viable. We're almost at the point where they fit a Macbook.
You're not thinking black-swan enough.
You're typing your comments using a blob of goo with about a hundred million neurons in it that cycles under a hundred hertz and draws less than 20 watts.
I don't think that we'll be running packs of goo in our PCs any time soon. But I do think some entirely different way of looking at the problem will emerge that will reduce computational requirements by many orders of magnitude. And it won't involve gigantic statistical engines trying to find the best average response to a question.
There are 86 billion neurons in the human brain and 16 billions in the cerebral cortex. More importantly, there are 100-1000 trillion synapses, and we know that parts the dendritic tree do independent computations as well. The computations are not synchronized with a global clock, but 1 ms events correspond to a kHz refresh rate and we know spikes can do temporal coding. Meanwhile, the best we can do is Cerebras CS-3 and with WSI at 5 nm there isn't much more where that came from.
Try doing the math on how many CS-3 you'd need to represent and refresh the above.