Comment by kennywinker
2 months ago
Doesn’t need to be a winner head to head. If it can do 90% of the tasks the big boys do, at 50% speed, for virtually no extra overhead cost save for the power consumed by a prompt - that’s gonna work for a lot of people. And that’s also basically where we’re at today. Qwen3.6 35b running quantized on 10 year old hardware solves basically all of my uses cases for agents except for coding.
The frontier models are faster, and better at coding, but not so much that i’ll pay $200/month for them.
Consider this. One of the smallest Qwen models (4B parameters) powers my home automation voice assistant, and runs on CPU alone at >20 tok/s. It is enough for that use case, and could be made even better/faster with a modest GPU. It isn't as smart as some cloud-connected thingamajig, but I would never allow a literal Google or Amazon bug in my home. Huge SOTA models aren't relevant everywhere. Most people use LLMs for rather trivial tasks such as finding typos or drafting text.
But with Apple's AFM 3 architecture, we might end up with huge SOTA adjacent on devices with limited RAM.
They use a technique where you only load between 1B and 4B of a 20B dense model for an entire prompt run, not token by token like a MoE, and use mostly the low power ANE instead of GPU cores.
Now, imagine if/when they scale up to 100B or more? On a chip using 2W?
I think we're also ignoring a potential innovative move in how models work.
If someone could splinter or fragment the models into more specific tasks i.e "spellchecker AI" and get these working as well as Sonnet 4.6-4.8 on those tasks on a personal laptop. You then question the $100 a month fee.
Bear in mind these laptops are likely to be $5000 or so because of the memory, HDD and M7 chip they likely need.
It feels to me like the beginning of the inflection point but software updates not hardware updates will be the accelerant.
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Curious, what exactly does it do for you? I has bad luck with these small models to do anything useful tbh.
It's a voice assistant that can respond to commands such as "turn on the light," or explain things (within the abilities of the small model).
> If it can do 90% of the tasks the big boys do, at 50% speed
I want to live in this world too, but these numbers, as of today, are very aspirational and far removed from reality.
I'm no tokenmaxxer; I find my modest local setup useful, I also know the limitations, it's slow and it sucks (relatively) at high-level and/or long-context planning, compared to frontier models. Only a minority of my prompts are max-effort - its not all I do, but, it also means frontier labs aren't dying any time soon
Consider also that right now LLMs run slowly enough you can watch them think. I've seen a demo of an LLM running at an absurdly high speed and it reminds me of when I moved from a 2400 baud modem to a 14.4 - BBS screens that I could watch draw were all of a sudden nigh-interactive. Faster-than-realtime video generation is also coming, and will also continue to require huge hardware for a long while yet.
I love local models - I have a machine at home that runs a few for me and it's a lot of fun - but for the time being they are not super trustworthy on tool calls and staying on script. Another year or so might change all that!
If anyone wishes to see the future. A fast LLM is quite eye-opening. I think chatjimmy uses Talaas' chips where models are hardcoded into the silicon.
https://chatjimmy.ai/
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What does your local setup look like?
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I’m sure you’re right, for the things you are asking of an llm, just as I am right about the things I am asking of an llm.
The real question is, what are 90% of people going to ask llms to do. I’d argue mostly it’s going to be stuff that works-now or almost-works on local models, but that’s just an opinion. It also depends on the frontier models hitting a wall of steeply diminishing returns, since they set the expectations for all of this stuff - my gut says that’s happened already they just won’t admit it for a while - but we’ll see.
This is what makes sense for me as well. All I need a local model is for playing with simple graphics: no gradients, at most ten colours which I can push through VTracer to get an SVG. Draw Things does the job, usually in 120 seconds or less.
Sometimes, I need a quick throwaway bit of python. That can take 30 minutes of my time.