It'd be cool to see more of this type of thing, but I have to imagine the ability for it to be updated to a brand-new model as new models come out is limited. If that is the case, it's going to be an extremely hard sell.
It really depends on the pricepoint at which they can get a board. If they can do a ~32B model for 1k$ and a size of an external HDD, I'd buy one now, even knowing that it won't be upgradeable / the model remains fixed. The speeds they've shown are a quality of its own, and there's plenty you can do with such a model and faster than instant responses.
Maybe in 10 years when the tech matures, but IMO now seems a bit too early to have a tech like this. It is like intelligence without evolution or progress.. yes it can be used in some niche markets, but difficult to be generic.
Yes, but with current architectures world knowledge is baked into the weights. We might stop figuring out how to make models better, but the world keeps changing, science is going to keep making progress at understanding the world, etc. This creates a significant minimum rate of change and I'm pretty skeptical that it's worth baking weights into silicon as a result.
I think it would just be an opportunity to sell another chip a few years down the line. If the utility curve flattens out on the performance of models I can see a future where you are buying an up to date chip every few years to upgrade to the latest and greatest, while providing up to date context as part of the user input. Like if I have a programming task and I supply a copy of up-to-date documentation alongside my input, I would think that I could still get good output out of a dated model.
I mean it just depends on the price of the chip. You might just replace the chip like you would any other component. Like a video game cartridge or something.
What makes you think that? The rate of change seems to have been increasing and there is so many chip and model best in different directions at the moment.
You don't need SOTA models for all tasks, and being able to do more routine tasks at something like 10% of the cost and 70x speed unlocks LLM use for things that are just unthinkable now (bulk classification tasks, real time speech interaction, etc)
I think the model they chose is out of date and hard to sell, but there are plenty of use cases where today's dumb small models are fine. A Qwen 3.5/3.6 or Gemma 3 model on silicon at those speeds would be genuinely world changing even if it's only 1-3B params. Such a model at those speeds will remain extremely useful even over a 5-6 year timespan, I think.
If you consider the places you could deploy it -- with no network access, and at those high speeds... very useful .. for adding vague "common sense" fuzzy thinking to all kinds of applications that right now piss consumers off with poor UX. Esp if the model can do voice-to-text and text-to-speech well (some of the smaller models can)
I wouldn't be surprised if "fast, cheap, dumb" end us being the market for LLMs.
The state-of-the-art models aren't at "can fully replace knowledge worker" levels yet and I doubt they'll get there any time soon, so charging $2000 / month for access isn't going to happen. Right now everyone and their dog is being handed subsidized credits to play with AI, but the actual outcome is rarely good enough to be worth the money they'd need to charge for it. It might very well take another order of magnitude or two to get LLMs to be truly good (if it is even possible at all), and considering how much money is already being pumped into it I just don't see that happening.
On the other hand, the dumb models are more than adequate for simple noncritical tasks, like directing a user to the appropriate FAQ entry, or playing phone decision tree. There's a lot of money in making chatbot assistants actually useful, or in augmenting website search. Turning it into a glorified "language-to-API-call" translator doesn't take a lot of smarts, but as long as it's cheap you can make a killing in volume.
> On the other hand, the dumb models are more than adequate for simple noncritical tasks, like directing a user to the appropriate FAQ entry
This is a lane I’ve been experimenting in —- seeing what I can get out of models that work in 16GB VRAM for simple tasks (screen scraping, decision tree navigation, natural language queries). It’s interesting for sure (certainly reveals non-deterministic limits) and promising for low criticality review-opportunity tasks, but I also feel like I need better sources/community for understanding and reflection. Preferably those that aren’t hype channels. Any pointers?
It really depends on the pricepoint at which they can get a board. If they can do a ~32B model for 1k$ and a size of an external HDD, I'd buy one now, even knowing that it won't be upgradeable / the model remains fixed. The speeds they've shown are a quality of its own, and there's plenty you can do with such a model and faster than instant responses.