"Baking in" a model into a chip is a bad idea because chips take 2 years to tape out and then you're stuck doing inference on llama 3 in 2026 when fable/sol are available. Every accelerator is a tradeoff between flexibility and performance and GPUs are already pareto-optimal
There are other options. I worked for a startup called NVXL and we were programming DNNs into FPGAs using OpenCL, on custom boards we built to plug into NVME. It worked great, but it wasn't fast enough at the time to compete with Nvidia, or even Intel AVX512. Ultimately the company failed, but maybe some hybrid like that could work for LLMs? I haven't been up to date on how DNNs and LLMs look like under the hood these days, but there's got to be someone doing something similar.
Well we'll see those surplus chips being repurposed for toys then. Who wouldn't want a new Furby that can actually hold a conversation.
It depends when the good enough level hits. Pretty sure we are almost there for most common applications of AI.