An ASIC won't help until models are compute bound. They're still memory bound at all levels.

Models are memory bound.

Startups burning through VC money to build massive warehouses full of hardware those models need to run on currently seem to be quite power grid connector bottlenecked. A more efficient chip would help.

Isn't GPUs a purpose made ASIC for parallell matrix math allready?

No? You can program a GPU, you can’t program ASIC. TPUs are closer to ASIC than GPUs.

> They are pointedly ignoring FPGA and ASIC. With the current model quality, would it really be so bad to burn Claude irrevocably on a chip and have a non-modifiable, cheap to mass produce, order of magnitude faster Claude-in-a-chip? This is what happened to Bitcoin ultimately, there are huge performance gains we know exist lying on the table just because they don’t exist for training. And even for training, TPUs make the first steps in that direction.

Yeah this is copium if I ever saw it.

That's the approach https://www.cerebras.ai/ is taking.

Inference is probably the cheapest part of it all.

The buildings will last for years, decades even, but the GPUs need to be replaced every 2-3 years in perpetuity.

True, but only because they are choosing to actively burn VC money on training.

But it’s not like the models we have now would stop to exist if training stopped. Other than the occasional retraining to get the latest data in, if they stopped wanton experimentation with models, that admittedly is pushing the models forward, the training costs could plummet and inference would be the thing to optimize and scale.

Without new models how would AI companies compete then?

Other features can easily be copied (eg Claude Code, ChatGPT, etc).

If AI services are a commodity then they can only compete on price.

Well, yeah. Which is where energy efficiency of FPGA and ASIC comes into play.