For the last five months or so, opus and fable have both been able to do this task as a soneshot prompt. An inference engine is almost pure compute and to a first approximation the only operation your hardware needs to support to do llm inference is multiplication [the rest of an inference engine is "some trig"].
So to get any model working on $OLD_HARDWARE, the hardest part of the task is now sourcing the hardware itself and giving the agent ssh access -- a modern agent will be able to source a compiler, write the implementation, optimize it, test in qemu (or any other simulator) and push it onto the device.
Example prompt: "write an inference engine for qwen3 0.6b in pure assembly that runs on this device. Write a script to attach the weights to the resulting binary. Prefix is supplied as a stream of u32 token IDs on stdin. The engine streams u32 token IDs on stdout."
I agree with you, and I'm using claude and other models heavily, the idea is not in the soneshot - because actually as you see in the logs I posted on github, it took around 107 + 50-ish experiments to figure out which models to run which and push the speed.
I'm treating it as a search algorithm project, where I develop the methdology and search for pushing the limit of devices and searching in a big space of possible kernels and so for what works for a specific hardware.
so I'm looking at it from a different prespective, it takes a lot of brute forcing, and claude can do this well, the whole game (to me) is which directions you push the model into to reduce the search space. and how that learning from very small and tight devices you can scale these techniques.
But in essence, I don't disagree with your sentiment - the goal is not 0.6B models, these are extremely trivial as you mentioned and as I showed in the experiments, the goal is 16B+ models, and this as far as I've seen, requires smart methods to search for optimizations and customizations both to the model and inference engines.
I just asked myself the same thing and wondered if this was some new technique that completely slipped past me. - But it certainly must be a double typo
So true, people think they can prompt AI, tell it to create something amazing and "Novel" so they can feel cool and bragg about it. Shame on such people.
Thanks for sharing! I'm exploring a similar direction, but optimising for extreme model size. I have gotten an LLM down to 970 KB, with some interesting results compared to GPT-2 (small).
Super interesting, not a CS/AI guy here, but I'm curious as to how you can train such a small-sized model give you as accurate an answer or good hypotheses to get you going
I'm planning to have a blog post up in the next two weeks or so (simedw.com), still have a lot of experiments to run, and training even tiny models takes a bunch of time.
This is very cool. Love the trend of optimizing small models
I’ve been working on small local models and it’s amazing how fast the tech has progressed
A year ago, I was creating conversational characters by finetuning a qwen 2.5 0.5b param model, and it worked pretty well. But no reasoning or math capabilities, and still required some decent specs to run; could maybe do well enough in a beefy raspberry pi
But now, I have a locally trained 6.5M param model that does reasoning, solves multi-step math problems, does simple physics problem simulation, tool calling, RAG and conversational chat. Total size about 30MB packaged for ONNX, and runs with <10ms latency in the browser
A year ago 1B parameter models couldn't do much, today with the correct harness and/or fine-tuning they can do more or less serious work (not frontier tho).
I believe in a 6-8 months it will get even better and my TV will be happy.
I didn't try fine tuning so small models honestly but looks interesitng to try.
Recently I started this open source project at my AI company, where I'm trying to make LLMs working
Now Reached 16B models running on iPad Air 1st gen and xiaomi tv box, both have less than 1GB of usable memory, and reached some decent speeds given the hardware.
I'm open sourcing the effort done, kernels developed and results for everyone to re-produce and push more, also the modified models are on hugging face : https://huggingface.co/hyperionaidev
I want a more challenging device, what do you think?
Aw man I would love to be able to use the crank to back up the agent dialogue, maybe make it branch forward in a different direction. Make the dialogue into a game
Yes this has been my exact thought for awhile now.
Perhaps the real value with AI is going to come from...surprise here...a whole bunch of different low resource AIs networked together living and working and yes playing thru a networked "society"
Raspberry Pis in general are too easy for this task they don't even need modifications, older iphones seem more interesting due to some work on comptability.
It's technically sweet regardless, so don't take this as even a little bit of negative pushback, but I am curious what the utility of these small models is? Are they smart enough to be useful on practical tasks? What are people doing with them?
If you work in a highly regulated space subject to e.g. HIPAA, and you work with ancient on-prem systems within a slow moving and risk intolerant organization -- for instance, virtually every state run healthcare system in the US -- then these are all you can hope for.
In these settings, data is insanely messy beyond your wildest nightmares and hidden away in free-text fields that small models like this are great at cleaning up, normalizing, parsing, categorizing, etc
Not op but my opinion is that It doesn't matter if it's not currently useful. I think it's important that the community focuses on this line of research because big companies will not. It's in their best interest to make LLMs resource hungry
Yeh; this. They see how GTA6 looks and know a hypothetical scenario like a future Nvidia 7000 series with 96GB VRAM and 512GB system RAM in everyone's home means the entire tech, media, knowledge work labor system; the rich peoples social moat; evaporates.
It's all quite clearly collusion among a few dozen, hundred. No way their education/experience or data analysis engines have not made them aware.
They’re just used to Boomer and Silent Generation politicians blithely enabling them cause line go up for them when they do!
Curious to see how well it works on Millennials and GenZ; the elders were educated before STEM education had matured in a lot of ways. Younger generations have not had debunked fantasy rammed down their throat as scientific fact; Nostradmus types of the mid-late 1900s pointing at some ignorant math assuming it's true because it's written in a scientific notation!
Duh!
Science education of the past gave rise to thinkers like the Oceangate CEO. Nihilists who figure there's no such thing as forever and we all die anyway; so YOLO no matter who I take down with me.
This is an interesting question, so the idea is, based on experience, fine tuned small models can do narrow tasks very well (my experience running models fine-tuned on customer data as low as 1-2B is very promising) also the goal is to find a breakthrough were instead of the line of work (which I respect) that tries to squeeze much bigger models into smaller hardware like strata and maya, I'm working backward to discover/search for custom kernels and modifications till we exhaust the limit of what a smaller machine is capable of, so instead of people doing 64gb machines and old gpus to run a deepseek, I want to see what is the biggest usable qwen small model can run with reasonable speed on smaller devices, and by scaling as we discover more tricks, normal laptops can unlock more compute - for example a qwen 3.8 27B can do pretty serious work when coupled with a specialized harness - but we are yet to figure out what is the smallest machine that can run it with usable output and usable speed, and this can unlock interesting business.
I mostly agree today, but I'm trying to push for the future where smaller models are more capabable, and I believe it will happen + the fine tuned cases I mentioned in other commments.
On other notes : there is no free lunch, you have to sacrifice something for the other
It shows using Per Layer Embeddings to run models that normally take 100s of MB of ram run efficiently in just a few KB of ram and keeps most of the model on disk. It’s somewhat similar to MoE so it doesn’t stream in the whole model from flash for every token, but takes it even farther. If some of these devices have 1GB of ram and a few GB of disk there might be some interesting options here.
I’ve been investigating getting something like that running on the Wii too, though I haven’t made much progress in a while. Some basic calculations says a 1B model might be possible, at least a few hundred M
well, the whole idea honestly is curiousity about limits of hardware more than actual application today plus the idea that in the future people can run more capable models (in terms of capability @ small parameters) on such devices.
It makes more practical sense today to just go into 4gb GPUs and many people are working on this already.. on my side of things, it's more on the exploration side for now, until we see what good models get released that can use this capability.
There are many people on github and x running decent models especally MoE models on older GPUs and it's doing nice, I'm personally tinkering with this at home.
Funny, I use that exact website to find passively cooled hardware to use as a server.
I did get Qwen3.5 0.8B working at 5 tokens per second on the iGPU of a Dell Wyse 7020. I mostly use it to complain to; I have not yet found an agentic task for it. But also the CPU does plenty of small services and terraria servers and such.
This is the lord’s work right here. Models at the the intersection of small and useful — forthcoming models, admittedly— will be in the toolset that the next generation of poor kids will use to build a more egalitarian world.
most of the problem is actually brute forcing, so I'm using a brute forcing machine and just giving it pointers to reduce the size of search space for tricks, still fun, nothing frontier I know, but it's nice to see what we can do with our little brute forcing machines.
The fact that you've commented two things under the same comment kinda feels like it's not your doing but the agents doing
mrashad74 3 minutes ago | parent | next [–]
most of the problem is actually brute forcing, so I'm using a brute forcing machine and just giving it pointers to reduce the size of search space for tricks, still fun, nothing frontier I know, but it's nice to see what we can do with our little brute forcing machines.
reply
mrashad74 4 minutes ago | parent | prev | next [–]
well, yes claude is doing that, and I'm directing it and building the experiments and direction only, I didn't hide it at all :)
Everyone is using LLMs now, so it's acceptable to use it for a fun experiment too I believe.
For the last five months or so, opus and fable have both been able to do this task as a soneshot prompt. An inference engine is almost pure compute and to a first approximation the only operation your hardware needs to support to do llm inference is multiplication [the rest of an inference engine is "some trig"].
So to get any model working on $OLD_HARDWARE, the hardest part of the task is now sourcing the hardware itself and giving the agent ssh access -- a modern agent will be able to source a compiler, write the implementation, optimize it, test in qemu (or any other simulator) and push it onto the device.
Example prompt: "write an inference engine for qwen3 0.6b in pure assembly that runs on this device. Write a script to attach the weights to the resulting binary. Prefix is supplied as a stream of u32 token IDs on stdin. The engine streams u32 token IDs on stdout."
I agree with you, and I'm using claude and other models heavily, the idea is not in the soneshot - because actually as you see in the logs I posted on github, it took around 107 + 50-ish experiments to figure out which models to run which and push the speed.
I'm treating it as a search algorithm project, where I develop the methdology and search for pushing the limit of devices and searching in a big space of possible kernels and so for what works for a specific hardware.
so I'm looking at it from a different prespective, it takes a lot of brute forcing, and claude can do this well, the whole game (to me) is which directions you push the model into to reduce the search space. and how that learning from very small and tight devices you can scale these techniques.
But in essence, I don't disagree with your sentiment - the goal is not 0.6B models, these are extremely trivial as you mentioned and as I showed in the experiments, the goal is 16B+ models, and this as far as I've seen, requires smart methods to search for optimizations and customizations both to the model and inference engines.
Why did you both say "soneshot"? Same typo of oneshot?
I just asked myself the same thing and wondered if this was some new technique that completely slipped past me. - But it certainly must be a double typo
orrr we can listen to paranoia that these 2 comments are just an elaborate advertisement
What is “soneshot”?
So true, people think they can prompt AI, tell it to create something amazing and "Novel" so they can feel cool and bragg about it. Shame on such people.
no one said it's amazing or novel, it's just a fun experiement I'm not selling this :) just out of curiousity to explore.
Thanks for sharing! I'm exploring a similar direction, but optimising for extreme model size. I have gotten an LLM down to 970 KB, with some interesting results compared to GPT-2 (small).
Super interesting, not a CS/AI guy here, but I'm curious as to how you can train such a small-sized model give you as accurate an answer or good hypotheses to get you going
This is extremely interesting, do you have a public repo or write up to share or not yet? if not yet please sign me up when you release it, Cheers!
I'm planning to have a blog post up in the next two weeks or so (simedw.com), still have a lot of experiments to run, and training even tiny models takes a bunch of time.
This is very cool. Love the trend of optimizing small models
I’ve been working on small local models and it’s amazing how fast the tech has progressed
A year ago, I was creating conversational characters by finetuning a qwen 2.5 0.5b param model, and it worked pretty well. But no reasoning or math capabilities, and still required some decent specs to run; could maybe do well enough in a beefy raspberry pi
But now, I have a locally trained 6.5M param model that does reasoning, solves multi-step math problems, does simple physics problem simulation, tool calling, RAG and conversational chat. Total size about 30MB packaged for ONNX, and runs with <10ms latency in the browser
A year ago 1B parameter models couldn't do much, today with the correct harness and/or fine-tuning they can do more or less serious work (not frontier tho).
I believe in a 6-8 months it will get even better and my TV will be happy.
I didn't try fine tuning so small models honestly but looks interesitng to try.
Recently I started this open source project at my AI company, where I'm trying to make LLMs working
Now Reached 16B models running on iPad Air 1st gen and xiaomi tv box, both have less than 1GB of usable memory, and reached some decent speeds given the hardware.
I'm open sourcing the effort done, kernels developed and results for everyone to re-produce and push more, also the modified models are on hugging face : https://huggingface.co/hyperionaidev
I want a more challenging device, what do you think?
> a more challenging device
A Playdate
Aw man I would love to be able to use the crank to back up the agent dialogue, maybe make it branch forward in a different direction. Make the dialogue into a game
https://play.date/
pretty interesting, I don't own one, but it's possible probably with an emulator to figure this out, thanks!
Yes this has been my exact thought for awhile now.
Perhaps the real value with AI is going to come from...surprise here...a whole bunch of different low resource AIs networked together living and working and yes playing thru a networked "society"
Sounds kinda familiar doesn't it?
A Speak and Spell that replies using the voice processor
perhaps the pi 1st/2nd gen?
I'd thought of the ESP32 devices but their RAM is too small
Pi 1 is too easy. My PicoLM [fork] already runs on it.
It also runs on MS-DOS 80386 2MB RAM, so...
And iPhone 1. And Digital UNIX.
Raspberry Pis in general are too easy for this task they don't even need modifications, older iphones seem more interesting due to some work on comptability.
> I want a more challenging device, what do you think?
One of those vapes with a screen on it.
challenge accepted.
literal vaporware?
It's technically sweet regardless, so don't take this as even a little bit of negative pushback, but I am curious what the utility of these small models is? Are they smart enough to be useful on practical tasks? What are people doing with them?
If you work in a highly regulated space subject to e.g. HIPAA, and you work with ancient on-prem systems within a slow moving and risk intolerant organization -- for instance, virtually every state run healthcare system in the US -- then these are all you can hope for.
In these settings, data is insanely messy beyond your wildest nightmares and hidden away in free-text fields that small models like this are great at cleaning up, normalizing, parsing, categorizing, etc
Not op but my opinion is that It doesn't matter if it's not currently useful. I think it's important that the community focuses on this line of research because big companies will not. It's in their best interest to make LLMs resource hungry
Yeh; this. They see how GTA6 looks and know a hypothetical scenario like a future Nvidia 7000 series with 96GB VRAM and 512GB system RAM in everyone's home means the entire tech, media, knowledge work labor system; the rich peoples social moat; evaporates.
It's all quite clearly collusion among a few dozen, hundred. No way their education/experience or data analysis engines have not made them aware.
They’re just used to Boomer and Silent Generation politicians blithely enabling them cause line go up for them when they do!
Curious to see how well it works on Millennials and GenZ; the elders were educated before STEM education had matured in a lot of ways. Younger generations have not had debunked fantasy rammed down their throat as scientific fact; Nostradmus types of the mid-late 1900s pointing at some ignorant math assuming it's true because it's written in a scientific notation! Duh!
Science education of the past gave rise to thinkers like the Oceangate CEO. Nihilists who figure there's no such thing as forever and we all die anyway; so YOLO no matter who I take down with me.
This is an interesting question, so the idea is, based on experience, fine tuned small models can do narrow tasks very well (my experience running models fine-tuned on customer data as low as 1-2B is very promising) also the goal is to find a breakthrough were instead of the line of work (which I respect) that tries to squeeze much bigger models into smaller hardware like strata and maya, I'm working backward to discover/search for custom kernels and modifications till we exhaust the limit of what a smaller machine is capable of, so instead of people doing 64gb machines and old gpus to run a deepseek, I want to see what is the biggest usable qwen small model can run with reasonable speed on smaller devices, and by scaling as we discover more tricks, normal laptops can unlock more compute - for example a qwen 3.8 27B can do pretty serious work when coupled with a specialized harness - but we are yet to figure out what is the smallest machine that can run it with usable output and usable speed, and this can unlock interesting business.
Speech recognition, speech generation.
This alone is quite useful in small/limited devices/contexts.
Realtime log parsing and summarization is another application I've seen for little LLM's..
Basically, anywhere you'd use an LLM, but at much, much smaller scales of ontology.
Also curious on this. They are too dumb to use with tools and if you ask it how to bake a pancake you only get rubbish.
I mostly agree today, but I'm trying to push for the future where smaller models are more capabable, and I believe it will happen + the fine tuned cases I mentioned in other commments.
On other notes : there is no free lunch, you have to sacrifice something for the other
in a similar vein, somebody got tinystories LLM running on the nintendo wii, including gpu acceleration [0].
0: https://github.com/brysontang/iiama2.c
this looks very interesting, it can't run a real (read : working on some real tasks) LLM, yet, but it's actually very interesting device.
Here’s another repo that might be interesting
https://github.com/slvDev/esp32-ai
It shows using Per Layer Embeddings to run models that normally take 100s of MB of ram run efficiently in just a few KB of ram and keeps most of the model on disk. It’s somewhat similar to MoE so it doesn’t stream in the whole model from flash for every token, but takes it even farther. If some of these devices have 1GB of ram and a few GB of disk there might be some interesting options here.
I’ve been investigating getting something like that running on the Wii too, though I haven’t made much progress in a while. Some basic calculations says a 1B model might be possible, at least a few hundred M
This is really cool man. I like this on going trend of resurrecting old tech with AI and this is yet another avenue to explore.
I'd be interested in seeing some older flag ship mobile devices
I have a few phones and tablets laying around, will try to play with..
So does it make sense to try and run models on older 4gb GPUs? I assume the energy efficiency isn't worth it?
well, the whole idea honestly is curiousity about limits of hardware more than actual application today plus the idea that in the future people can run more capable models (in terms of capability @ small parameters) on such devices.
It makes more practical sense today to just go into 4gb GPUs and many people are working on this already.. on my side of things, it's more on the exploration side for now, until we see what good models get released that can use this capability.
There are many people on github and x running decent models especally MoE models on older GPUs and it's doing nice, I'm personally tinkering with this at home.
What can these small models do? Are they just for novelty?
as of now, it's just for novelty and fun, without serious fine tuning on domain data, you can't really do much with the smaller models.
> Device #1 is a Xiaomi TV Box S (3rd Gen): Amlogic S7D, 4× Cortex-A55 @ 2.5 GHz, 1.9 GB of RAM (about 1 GB usable)
Whoah!! A tv has almost 2GB of ram?! Does it need it to run Chrome or Electron? /s Ouch
machine spirits are coming for my calculator
This is great. I've recently been playing with repurposing some old thin clients as homelab Linux machines
Not my site, but same idea: https://www.parkytowers.me.uk/thin/
All this old/obsolete gear is plenty capable when used with the right software.
Funny, I use that exact website to find passively cooled hardware to use as a server.
I did get Qwen3.5 0.8B working at 5 tokens per second on the iGPU of a Dell Wyse 7020. I mostly use it to complain to; I have not yet found an agentic task for it. But also the CPU does plenty of small services and terraria servers and such.
https://nortyspock.github.io/blog/running-a-language-model-o...
Out of interest, what have you been doing with them?
Running a pihole, a WeeWx weather station, a NAS backup server, a wireguard VPN, Zot container registry.
This is the lord’s work right here. Models at the the intersection of small and useful — forthcoming models, admittedly— will be in the toolset that the next generation of poor kids will use to build a more egalitarian world.
It appears that it's not you doing that but claude doing that.
That's programming in 2026. Right now, Claude it doing my job for me, but if it goes well enough I'm the one who's going to be promoted.
most of the problem is actually brute forcing, so I'm using a brute forcing machine and just giving it pointers to reduce the size of search space for tricks, still fun, nothing frontier I know, but it's nice to see what we can do with our little brute forcing machines.
The fact that you've commented two things under the same comment kinda feels like it's not your doing but the agents doing
mrashad74 3 minutes ago | parent | next [–]
most of the problem is actually brute forcing, so I'm using a brute forcing machine and just giving it pointers to reduce the size of search space for tricks, still fun, nothing frontier I know, but it's nice to see what we can do with our little brute forcing machines.
reply
mrashad74 4 minutes ago | parent | prev | next [–]
well, yes claude is doing that, and I'm directing it and building the experiments and direction only, I didn't hide it at all :)
Everyone is using LLMs now, so it's acceptable to use it for a fun experiment too I believe.
reply
not really it's just a mistake of my side replying to differnt comments - I don't not use any agents for interacting online :D
well, yes claude is doing that, and I'm directing it and building the experiments and direction only, I didn't hide it at all :)
Everyone is using LLMs now, so it's acceptable to use it for a fun experiment too I believe.
ESP P4 or ESP S31?
challenge accepted, I have one lying around, will see.