alt Hacker NewsThis would be a very interesting future. I can imagine Gemma 5 Mini running locally on hardware, or a hard-coded "AI core" like an ALU or media processor that supports particular encoding mechanisms like H.264, AV1, etc.
Other than the obvious costs (but Taalas seems to be bringing back the structured ASIC era so costs shouldn't be that low [1]), I'm curious why this isn't getting much attention from larger companies. Of course, this wouldn't be useful for training models but as the models further improve, I can totally see this inside fully local + ultrafast + ultra efficient processors.
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> I'm curious why this isn't getting much attention from larger companies
I would be shocked if Google isn’t working on this right now. They build their own TPUs, this is an extremely obvious direction from there.
(And there are plenty of interesting co-design questions that only the frontier labs can dabble with; Taalas is stuck working around architectural quirks like “top-8 MoE”, Google can just rework the architecture hyperparameters to whatever gets best results in silico.)
Well even programmable ASICs like Cerebras and Groq give many-multiples speedup over GPUs and the market has hardly reacted at all.
Apple should have done this yesterday. A local AI on my phone/Macbook is all I really want from this tech.
The cloud-based AI (OpenAI, etc.) are todays AOL.
> I'm curious why this isn't getting much attention from larger companies.
Time is money and when you're competing with multiple companies with little margin for error you'll focus all your effort into releasing things quickly.
This chip is "only" a performance boost. It will unlock a lot of potential, but startups can't divide their attention like this. Big companies like google are surely already investigating this venue, but they might lack hardware expertise.
> I'm curious why this isn't getting much attention from larger companies.
I can see two potential reasons:
1) Most of the big players seem convinced that AI is going to continue to improve at the rate it did in 2025, if their assumption is somehow correct by the time any chip entered mass production it would be obsolete.
2) The business model of the big players is to sell expensive subscriptions, and train on and sell the data you give it. Chips that allow for relatively inexpensive offline AI aren't conducive to that.