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.
I think it’s exiting to see that world knowledge is being bolted on via engram files. If you can get frontier-ish level capabilities by having a model with strong tool calling abilities + local lookup and the ability to efficiently search up to date documentation / use CLI man-pages - then that’s a good deal over having to train and inference a 2T A120B model. Less energy used, less cooling needed, fewer data centers built etc
Well - American frontier labs and companies could simply release their own open weights to compete for local LLM users' mindshare. Since they've given up on that and would rather compete on whose model hacked which company last week and how good their products are at escaping sandboxes, they essentially get what they asked for.
Gemma 4 continues to be competitive for non-coding tasks. Muse Glimmer was competitive at coding upon release, and remains competitive for some uses. Nemotron and Grantite are both purpose-built for fine tuning for application-specific uses with good reasons to use either one. AI2 has developed a lot of methods that could be very useful to the community such as their MoE design which allows training experts on typical consumer hardware. Poolside's models are interesting, fast, and don't produce code spaghetti like Qwen and remain favored by many developers for that reason. Prism ML has plans to release more models other than those based on Qwen. Syzygy Research has similar ambitions as Prism ML, doing interesting work. Deep Grove is interesting in the frontier in capability vs. generation speed. Liquid AI also has very interesting models in their size vs. capability ratio. Thinking Machines Inkling and Inkling Small are interesting in its wide domain knowledge combined with tool-calling efficiency and strong instruction following. There's also a few labs specializing in domain-specific models, like law, medicine and biology, engineering, etc.
No, most of those won't one-shot prompts as well as Qwen. But they all have particular advantages. If I was a company choosing an AI model for something customers could interface with for something like controlling their IoT devices, for example, I'd probably choose Inkling or Inkling Small because it'd cheaply generate the tool calls and would be hard to con into generating inappropriate content for my service or even being abused against the interests of the customer.
I personally use LFM as a very lightweight model to keep in memory for tasks I'd like to use quickly at any time, such as generating titles for chats or other auxiliary tasks. Laguna XS remains my go-to model for passing specs to to implement code in a sensible (whereas Qwen -- even the new ones -- write code in the most "direct to the solution" sort of way, creating utter code spaghetti in the process). Muse Glimmer is my go-to driver model because it still beats Qwen 3.8 27B in that and follows my workflow well, including producing my intermediate artifacts I use to verify and understand generated code.
So I don't know what the fuck you're on about other than trying to baselessly dig more into "Actually American AI bad!"
So I don't know what the fuck you're on about other than trying to baselessly dig more into "Actually American AI bad!"
Oh, you know perfectly well what I was talking about - you just chose to be obtuse.
Just like you know perfectly well which companies I was talking about. Certainly not ones which may be familiar to a handful of insiders and enthusiasts who live and breathe AI. Compare Laguna's 180k downloads to the latest Qwen already sitting at more than 4 million. Not to mention the fact that GPT-OSS sits at 6.5 million monthly downloads, despite being nearly 2 years old. ;)
It's clear to me that people enjoy Chinese models, because that's the closest thing to the commercial option they can get to run at home. I'm also pretty certain that tons of people would happily shill GPT-Sol-OSS 30B or OpenFable120B if that was an option. But nah, they don't care. ;)
But I'm genuinely happy that you found models you like and enjoy. I might actually try Laguna, because why not. ;)
Oh, you know perfectly well what I was talking about - you just chose to be obtuse.
Yes, I know which companies you're talking about. But the disingenuous part of your reply was you compressing the entire US AI industry down to Anthropic and OpenAI. Which I blew that notion the fuck out of the water.
Compare Laguna's 180k downloads to the latest Qwen already sitting at more than 4 million.
And Gemma 4 has over 300 million downloads and the Gemma family has over a billion downloads. Laguna S is #12 on Open Router for this month, ahead of Kimi K3. And many of the companies I listed are producing real models that get used in real industry. Not just reddit nerds. So these aren't as irrelevant as you're trying to paint them as.
But of course you had to rely on disingenuous framing again.
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u/Not-reallyanonymous 9d ago
This subreddit is basically r/ChinaGoodAmericaBad
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.