I wonder how far down they can scale a diffusion LM? I've been playing with in-browser models, and the speed is painful.
But I wonder how Taalas' product can scale. Making a custom chip for one single tiny model is different than running any model trillions in size for a billion users.
Roughly, 53B transistors for every 8B params. For a 2T param model, you'd need 13 trillion transistor assuming scale is linear. One chip uses 2.5 kW of power? That's 4x H100 GPUs. How does it draw so much power?
If you assume that the frontier model is 1.5 trillion models, you'd need an entire N5 wafer chip to run it. And then if you need to change something in the model, you can't since it's physically printed on the chip. So this is something you do if you know you're going to use this exact model without changing anything for years.
Very interesting tech for edge inference though. Robots and self driving can make use of these in the distant future if power draw comes down drastically. 2.4kW chip running inside a robot is not realistic. Maybe a 150w chip.
> The first generation HC1 chip is implemented in the 6 nanometer N6 process from TSMC. ... Each HC1 chip has 53 billion transistors on the package, most of it very likely for ROM and SRAM memory. The HC1 card burns about 200 watts, says Bajic, and a two-socket X86 server with ten HC1 cards in it runs 2,500 watts.
https://www.nextplatform.com/2026/02/19/taalas-etches-ai-mod...
> Following this, a frontier LLM will be fabricated using our second-generation silicon platform (HC2). HC2 offers considerably higher density and even faster execution. Deployment is planned for winter.
From https://taalas.com/the-path-to-ubiquitous-ai/
Personally I think anything around the level of Sonnet 4.5 is worth burning to silicon because agentic workflows work. There are plenty of places where spending $50,000 for that makes sense (I have no idea of the pricing though)
I'd take an army of high-school graduate LLMs to build my agentic applications over a couple of genius LLMs any day.
This is a whole new paradigm of AI.