This is something I've been fantasizing about for long.
Let's say we took Rust, a language that makes parallelization easier than others (as it helps you avoid some common footguns). How difficult would it be to have a massively parallel computer system made out of many tiny, simple microcontroller-like chips? Let's say we picked many little Risc-V's. Surely this would be an interesting experiment (though I'm not sure whether it'd make economic sense or not...)
It would certainly not make any economic sense, and I guess that's also why noone is seriously looking into stuff like volunteer/enthusiast clusters of home computers to do inference in the same way that e.g. LHC@home works. The main bottleneck for LLMs is still memory bandwidth. Any memory bus not directly soldered on your GPU is terribly slow. That's why one big GPU with twice the VRAM will always perform significantly better than two GPUs with half the VRAM each. And it's also not like you can just solder more memory onto a chip. At modern speeds, the speed of light is a hard limit. For current GDDR7, signals may only travel like 10mm per cycle.
If you spread such a system out over dozens or hundreds of tiny chips, you'll be wasting most of its resources and lose hard to anyone who built a single chip setup.
You know, I've always liked Futurama but I always kind of thought it was silly that literally everything has an AI and a personality.
But, you know, I actually think that there might be a logic to it. Economies of scale might mean that almost-literally every computer you buy in the year 3000 has some kind of AI-assistance chip in there, and sure maybe it will have full AI with a personality spitting out one-liners.
I'm curious how this would handle grammar checking on a basic word processor. Or maybe generate worlds for small text based games. I have no idea what the capabilities are of a cluster like this.
Seriously though, what are the low cost chips that can usefully run LLMs? Is a Mac Mini the lowest we can go? Are there iGPUs on mini-itx that can do it, or are there dedicated AI chips that one could turn into a pi HAT?
This is something I've been fantasizing about for long.
Let's say we took Rust, a language that makes parallelization easier than others (as it helps you avoid some common footguns). How difficult would it be to have a massively parallel computer system made out of many tiny, simple microcontroller-like chips? Let's say we picked many little Risc-V's. Surely this would be an interesting experiment (though I'm not sure whether it'd make economic sense or not...)
It would certainly not make any economic sense, and I guess that's also why noone is seriously looking into stuff like volunteer/enthusiast clusters of home computers to do inference in the same way that e.g. LHC@home works. The main bottleneck for LLMs is still memory bandwidth. Any memory bus not directly soldered on your GPU is terribly slow. That's why one big GPU with twice the VRAM will always perform significantly better than two GPUs with half the VRAM each. And it's also not like you can just solder more memory onto a chip. At modern speeds, the speed of light is a hard limit. For current GDDR7, signals may only travel like 10mm per cycle.
If you spread such a system out over dozens or hundreds of tiny chips, you'll be wasting most of its resources and lose hard to anyone who built a single chip setup.
Soon ai in every lightbulb running Kubernetes
Praise the Omnissiah.
With the proliferation of Abominable Intelligence? Quite the contrary!
You know, I've always liked Futurama but I always kind of thought it was silly that literally everything has an AI and a personality.
But, you know, I actually think that there might be a logic to it. Economies of scale might mean that almost-literally every computer you buy in the year 3000 has some kind of AI-assistance chip in there, and sure maybe it will have full AI with a personality spitting out one-liners.
Kind of like how disposable vape pens often have a 24 MHz Cortex-M0+ with 3 kB SRAM and 24 kB flash, which would have seemed ludicrous a while back.
Change the year 3000 to the 2030s and it might be just as accurate.
haha … this is precisely the kind of project that https://bil-lang.org is aimed at: Go for parallel (ie in this case pipeline processing).
don’t get too excited until we get the TinyGo backend built though ;-)
I'm curious how this would handle grammar checking on a basic word processor. Or maybe generate worlds for small text based games. I have no idea what the capabilities are of a cluster like this.
Seriously though, what are the low cost chips that can usefully run LLMs? Is a Mac Mini the lowest we can go? Are there iGPUs on mini-itx that can do it, or are there dedicated AI chips that one could turn into a pi HAT?
It is a bit of a bummer to see that the degree of 'compression' makes it a fancy llm noise-maker. It is still charming.
I’m actually working on a small project that’s exactly this! Less quant so it’s only 150M parameters but this is amazing.
Gemma 4 when?
We're gonna need a bigger ESP.
Thanks for sharing. It's fascinating to see a 0.5B LLM being split across seven ESP32s like this.