First off the math doesn’t math. Datacenters are willing to pay $50k for a single high end GPU. If you have unlimited capacity, yeah sell millions for $100 a pop or $10 a pop or whatever the bom cost of a phone GPU would be - but if you have limited capacity, you’re gonna sell all of that to the customer who is willing to pay the most PER UNIT.

Second off, this doesn’t work from a power consumption standpoint. When I run qwen3.6-35b, a far smaller model than op is suggesting, power usage spikes to 150-200W during inference. To fit a 1T model in the palm of my hand, the amount of processing required doesn’t fit the amount of power available.

Now I’m not saying this will never happen - there are some great leads, e.g. burning models directly on to a chip - but op’s scenario is definitely not happening in two years. Maybe 5, a lot more likely 10, unless of course local ai is made illegal

▲ typon 2 days ago | parent | next [-]

There is ton of room for improvement "down there".

* Software inference optimizations

* Heavy quantization

* Chips with hardcoded transformer architecture

* Much cheaper HBM

* Much sparser models - 1T total with ~1-10B active params e.g.

* Not to mention - 2 years of today's frontier models writing RTL and kernels at superhuman levels.

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kennywinker 2 days ago | parent [-]

> * Software inference optimizations

Absolutely. I'd be surprised if they couldn't 2x performance in the next year. Still doesn't make a 1T model fit on your phone.

> * Heavy quantization

I think this is a dead end if you're trying to fit a 1T model into a phone. Makes much more sense to train a model that's designed to be small, than train a model that's smart and then quantize it into stupidity.

> * Chips with hardcoded transformer architecture

Totally, this will probably work great. Now good luck booking fab time any time in the next 2 years.

> * Much cheaper HBM

Totally, this will probably work great. Now good luck booking fab time any time in the next two years.

> * Much sparser models - 1T total with ~1-10B active params e.g.

Fewer active params helps with the speed of token generation, but if the whole model doesn't fit into ram it doesn't solve the issue of having to constantly stream portions of the model from disk to ram.

> * Not to mention - 2 years of today's frontier models writing RTL and kernels at superhuman levels.

IMO this is a delusional myth-making idea being sold to us by ai companies. Machines that generate output based on statistical averages won't generate genuinely new ideas. They can help us try out ideas faster, but they're simply not capable of the kind of creativity and understanding required to push a field forward, except incrementally.

	▲	iwontberude a day ago \| parent [-]
		We dont need god models (1T+) to do dog work. People use far more powerful models than they ever need to, but they don’t even know it yet. They create fake demand with FOMO.

▲ iwontberude a day ago | parent | prev | next [-]

You are assuming people need the models they use today. The reality is much much smaller models will suffice (i.e. dont use god models for dog work)

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kennywinker a day ago | parent [-]

I’m not assuming that at all, I’m responding to someone suggesting we’ll be able to run 1T models on phones in 2-ish years.

I absolutely agree that models are going to advance on to “edge” hardware over the next few years by becoming small + specialized.

	▲	iwontberude a day ago \| parent [-]
		Sorry to put words in your mouth, totally agree

▲ andrekandre 2 days ago | parent | prev [-]

  > Datacenters are willing to pay $50k for a single high end GPU.

its true for now, because capital is flowing like a torrent, but how long will that last if returns start to be expected (aka the bubble pops)?

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kennywinker 2 days ago | parent [-]

Even if the bubble pops and anthropic and openai et al implode - genie doesn’t go back in the bottle. The usefulness of LLMs for coding is proven, and a chip in a datacenter running 24/7 is always going to be more valuable than in a personal device running occasionally.

That doesn’t change until production capacity exceeds the datacenter demand. When that happens, they’ll start selling them down the market until it eventually reaches phones and toasters and whatever. But not in two years.

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iwontberude a day ago | parent [-]

LLMs for coding is too small of a benefit to justify this investment, the bubble is indeed going to burst. Genie is already on its way back into the bottle.

	▲	kennywinker a day ago \| parent [-]
		I agree it’s too small a benefit to justify the investment, and I agree the bubble will pop. I just don’t think that means hardware prices become sane again for quite a while. I think if you half the price of a server GPU because demand from the big ai companies drops out, we’ll still have a shortage - it’ll just being going into commodity data centers to run open weight models.