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this post was submitted on 22 Sep 2026
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Gonna take GPS example to explain general processors vs specific ones.
GPS works by doing some simple math around triangulation from satalites. When they first came out there were no GPS chips. It was done by a general processors.
Now they can do it, but because they can do Amy Hong they're not as fast as newer GPS chips.
How they work is the silicon can only handle the GPS calculations. But because of that, it can do it way faster.
LLMs run on matrix math and probabilities. We can 100% come up with specialised silicon to handle this math. Intact, thats what a lot of AI specialised chips are.
NPUs are already a thing, specialized cpus for nerual networks. The problem is the models are just databases, and you need lots of fast memory in order to feed the NPUs data, and thats the current bottleneck.
They aren't comparable. GPS is deterministic and simple so as you said it could be reduced to an FPGA or ASIC.
LLMs are partially matrix math and probabilities but they're also more complex than that and require a large amount of RAM to run even the first time. Each query added to the context increases the RAM needed further.
I dont believe the memory, both ram and storage combine, are what's causing data centres to gobble up power and associated resources (electricity, cooling,...).
Its the GPUs.
I'm not counting on a future with LLMs by any means, but there's tons of efficiencies we can do to make the cost go down. Were just not doing it.
We saw the same thing with crypto mining. It started on GPUs but then speficially designed silicon (asyc miners) were vastly more efficient than a GPU rig.
It still needed memory yes, but again, thats not the constraint.
LLMs are also deterministic and the weights for a model could be 'hard-coded' into a chip, no?
They're actually not because they have randomness built in. Since we are working with probabilities, it won't always pick the next token that has the highest probability and the randomness can be tuned via a "temperature" setting to make it more or less likely that it will choose the most probable token.
The weights for the model could be stored in a firmware chip but you still need RAM because it pulls all the weights into RAM in order to perform the calculations.