alt Hacker NewsI think they talked about this being general purpose chip but I would think that Anthropic/OpenAI are at the scale now they could bake LLM weights into chips themselves.
For example, GPT Sol baked into a custom chip run for $100M that runs 10x as fast and 10x as cheap should pay for itself as long as the chip is useful for long enough.
While 2 years ago nothing was useful more than 1 year long, there are many older models in use now (e.g. Haiku 4.5, GPT-OSS 120b), and I expect this trend to continue.
I know this is what Taalas was doing (acquired by AMD), here was their demo, https://chatjimmy.ai/ which is based on Llama 3.1 8B. It feels like this should start to happen soon.
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> I know this is what Taalas was doing (acquired by AMD), here was their demo, https://chatjimmy.ai/ which is based on Llama 3.1 8B. It feels like this should start to happen soon.
Taalas needed a giant chip (6nm) for an 8B model.
At best you could use a more advanced node to try to put a MoE model across several chips working together, but you can’t have GPT Sol size models on a single chip like that.
"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
Yes, they could also sell me GPT Sol 5.6 or 5.7 on a chip and I’d probably buy it. It’s a really really useful model for me, I’m not sure how much better for coding I need it to be. For most things I find Sol good enough with a small amount of coaxing around my tastes.
My guess is we only see this once they start saturating computer use benchmarks. That's a use case which would be extremely valuable at the right costs/speed, but the current models just aren't there yet.
Probably! But not viable yet; the chips would be about a year behind SOTA. Note the ~16 months that the article quotes as being insanely fast to get this chip to tape-out (read: start producing). We'll have to bootstrap our way there: AI is actively being used to get us closer to viable lead times for this.
Unfortunately, there's some real physical constraints: IIRC, manufacturing a wafer takes on the order of a month, start to finish, for the physical processing.
Maybe once LLM improvements asymptote further?