But as is, it's probably hard to become popular amongst hobbyists and local model enthusiasts like Qwen's 27B class models or MOE models because it's a 65B dense model and inference will be prohibitively slow on consumer grade hardware, and seems like the goal/mission of the company behind this model is to automate chip design.
So if I have to assume and connect the dots, this is for the companies who want to use AI in air-gapped environments for chip design so that they protect their IPs (not leaking to third party AI labs/hostile parties) and can afford lots of server grade inference hardware for a quick unlock with the big dense model.
Neat!
Look at "SC117/Qwen3.8-Flash-Next-GSQ-RCO-abliterated-GGUF" on HuggingFace, It says it has 3.5M downloads this month and its offering a model that is 2-bit and 3-bit quantized (66GB - 83.9GB).
If this gets quantized to 4-bit (Q4_0) which usually there is very little loss in intelligence compared to the base model, then the model will probably be around 40GB, and looking that people are downloading a model that is at least 20GB bigger, then it might allow even more people to run a bigger model.
But it will only be popular among hobbyist if its as good as it claims to be.
What happens instead is that the "dense" parts of the model like e.g. the router are put into VRAM and the remainder of VRAM is occupied by the KV cache and whatever experts still fit.
The rest - usually just the experts - reside in RAM, but it's bo big deal.
Additionally, some models utilise an n-gram table, of which only one row is read per token, so it can actually be stored on disk without issue. Example:
https://huggingface.co/ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RC...
They are playing catch-up, but I’m happy to see new players to the game, and it will be interesting to watch how they develop
The bar chart also shows Nemotron 3 Ultra instead of smaller and better Nemotron 3.5 Super.
Seriously though, it's just easier to pretend that China doesn't exist when they're stomping us this badly.
https://www.theinformation.com/articles/openais-astra-model-...
For reference Qwen 3.8 Flash Next (which is bigger than this model but probably cheaper to run due to MoE) scores 84.3, and Qwen 3.8-27B scores 73.0.
I think this model could be competitive, depending on how much it was benchmaxxed compared to Qwen?
I think we will find the market is going to move into omni multi-modal models: text, moe, agentic tasks, voice, images, and video all in one LLM.
It's very well within the capabilities of any reasonable organization, maybe even crowd-funded.
Is this sponsored by the us gov? did it only train on US data? does it only work on the US?
You mean it's state of the art for this subset of benchmarks you're targeting? No? Well surely it's state of the art for models of a certain size category? Huh, what about models that are available for local inference? O, state of the art for open models of a certain size made in the US specifically. Uh. Neat!
> Introducing Ripple-65B Preview, a Frontier Open-Weight Model
It's not "frontier", it's not "frontier" even in the category of open-weight models (though usually that phrasing would mean frontier over all, and also open weight)... it's only "frontier" in the small sub-category of US made open-weight models.
Being at the Pareto frontier means that you can't get something better on one aspect without compromising on some other aspect. The trick is that it is easy to be at the Pareto frontier by having enough dimensions.
Here, we have 4: score, number of parameters, origin (US) and open-weight. You can get a better score or a lower number of parameters, but you have to compromise on at least one of the other aspects. That's a pretty weak frontier if you ask me.
(You could say that “ability to be trusted by US government/military users” is quantifiable, but I would argue that by that metric, this model falls behind non-US-originated open-weight models that have more eyes on them!)
By that logic, any model is a frontier model. My GPT-2 clone is a frontier model when you compare it against models that were trained on my homelab.
I guess it makes sense - there is a segmentation where users may want to run on domestic "frontier LLMs."
[Edit i think the preferred combination for those who actually want some data security at the moment and arent at the scale of self hosting llms is chinese open weight models served in germany or France where theres some better data privacy/security]
A tinyurl pic linked by randoms over the internet?
It was already meaningless when US labs used it.
It means new.