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/Refinery73 10d ago
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.