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this post was submitted on 22 Sep 2026
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I like LLMs and also the research but I think we're at a point where one could make a pause, turn the models into silicon and sell them as hardware for people who are actually interested.
That would solve all privacy problems, eliminate many data centers, subscription fees, internet dependencies and allow for slower and more thoughtful upgrades (hardware isn't updated as often as software). That would be a pretty sovereign solution.
However, from a company perspective they can't force it on everyone anymore, don't get all the juicy data from all the users and can't sell subscriptions any more and change everything on their side.
The models are data, and silicone is rubber. So... I don't know what you're proposing here.
Fixed it. We don't use this silly silicon word here. I picked the wrong one. We call it Silicium Sodium is Natrium, Potassium is Kalium, Aluminum is Aluminum etc.
I believe he is suggesting we turn them into very large breasts, or in some cases big booties, depending on your preference and what part of the world you're in.
That is correct. Very comforting
Might mean "silicon", but might want to bang the LLM. Hard to know.
Words have meaning...
Even with "silicon" doesn't really make sense. The models already live on silicon, they're stored in the datacenters needed to run them. It's not like you can make them into some chip and magically not have to use the datacenter.
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.
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.
That would be lovely