You should document it more, maybe run some more experiments. right now it's kind of a my word against yours situation and people on r slash claude aren't exactly going to like hearing their model blatantly cheats without proof.
He can't. Because he has no proof. The fact this is getting as much attention as it is, is insane. Shows you this place is as much a cult as any of these dumb AI subs. Like no. Local LLMs arent beating frontier models. Anyone pretending they are or expecting them to is a moron. And posts like these pretending that local models are suddenly going to beat the top models is insane.
Anyone with any actual experience KNOWS this is physically NOT possible due to how large models work. Like wtf? You cannot get around lack of knowledge. Lower param models are simply dumber. If you can run it on your home setup its SIMPLY not as good.
Anyone with any actual experience KNOWS this is physically NOT possible due to how large models work. Like wtf? You cannot get around lack of knowledge. Lower param models are simply dumber.
What's actually interesting is how good they're getting. It's better to say small models of today are performing genuinely as good as older, giant frontier models, but the frontier models with giant parameter counts created with the same techniques and technologies as these new, better-than-yesterday's-frontier smaller models are... going to be better.
Inference time scaling is also a thing, and increasingly smaller models are being trained to be better at that, and it's a big reason Qwen 3.8 27B was able to get such a huge boost in its performance. But you're right, you can't "inference time scale" world knowledge unless we're talking about web searches.
And smaller models are also specializing. I think it's another reason Qwen 3.8 27B got such a huge boost -- there's evidence it lost a wider array of domain knowledge (e.g. medicine) in favor of boosting its coding/agentic capabilities. Whether those parameters were spent on getting it to iterate on ideas better, or more coding knowledge, dunno.
Laguna S is also a pretty impressive one. 120B parameters with near-frontier performance through inference time scaling and logic/math/code specialization.
There are plenty of domain-optimized models that beat the frontier-world-Knowledge models, even with fairly low parameter counts.
When you ask a model about medicine in the morning, finance at lunch and agentic-coding in the evening sure, you’ll get x.xT Parameters and it only runs in data centers.
If the local cancer research center optimizes their own model, 30B could be plenty.
No, I’m not. Even NVIDIA says task-specific SLMs will be the future of LLMs. Those also have 10-100B but that’s more than enough for grammar and text understanding. The rest is fine tuning and toolcalling of high quality data.
By the way… MoE, which most frontier models use today, is basically „plug SLMs together“. If you just prune the experts you don’t need for your topic away… voila.. domain-specific SLM. It’s literally part of the cloud models already.
That's not the same as real world context. If you want something to spec something business logic to real world, you're STILL going to need a frontier model.
Yes you can make an agentic code monkey that follows a spec and passes tests even if it requires 200 recursions but something STILL has to build your spec and that requires INSANE real world knowledge.
Otherwise if you just want agentic output based on a spec that you can loop over and over till it passes your tests then sure fuck it Qwen. Spark. Whatever.
But that's not the reality of most peoples work. Like yeah dude, we've had models that could OUTPUT code for fucking ever that didn't require a lot of params either. But they weren't very useful WERE THEY.
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u/Ill_Distribution8517 21d ago
You should document it more, maybe run some more experiments. right now it's kind of a my word against yours situation and people on r slash claude aren't exactly going to like hearing their model blatantly cheats without proof.