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]
For one, you might not trust that the weights do not contain ideological biases (e.g. ask DeepSeek about tibet) or that the methods used to impose these biases may not have contaminated the model in some other way.
For another, you might not trust that the code the model generates won't be backdoored or attempt to leak information through your sandboxes.
For a third, you may not want to build your stack on a partner who you feel is a supply chain risk, who might become unaligned with your interests.
By far the biggest though, is that many industries will be forbidden from doing so by the government in an attempt to protect the US model industry (and thus create the demand for some model like this one).
I'm always excited to see new entrants to this open-models race, no matter why they're running.
And your speculation about future protectionist regulation is just that, not an actual fact.
I don't disagree with you on the severity of the concerns, but given the vast amounts of code that must have been generated in the past six months by the likes of GLM5.2, GLM5.3, deepseek v4 flash 0731 and newer, qwen 3.5-122, 3.6 and 3.8 series models, I would like to see any concrete examples of it building backdoors/intentional vulnerabilities/phone-home-to-china or similar unprompted. If it's been doing this and anyone with even a modicum of a clue has been watching it, surely someone has caught a model doing this? At least once?
Surely a model doing something like writing intentionally obfuscated code that obscures its actual purpose from a code reviewer, in the service of something malicious/backdoor-like, would have been seen and noticed by now.
Weights aren’t source that can be inspected for nefarious tricks that would impact use, so, to the extent that there is a dinension of trust associated with nation of origin, being open weights doesn’t obviate the concern (I would argue, though, that there is very little reason to for nation of origin to be a significant factor in trust alone, though a finer grained consideration of origin might be sensible.)
Well, you might want to know what propaganda/censorship you are subjected to. Case in point, a regional government in Austria just learnt that their AI doesn't know about the Tiananmen Square massacre.
Yep, you’re right. Quite easy to dilute the term.
A tinyurl pic linked by randoms over the internet?
It was already meaningless when US labs used it.
It means new.
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...
Those days engines try to optimize "whatever experts" part and can use remaining VRAM as cache, moving hot experts to GPU during inference (but less used experts keep executing on CPU, yea).
e.g. llama.cpp recently added flag for that: https://github.com/ggml-org/llama.cpp/pull/29887
Even If I set the fit target to a value which without the cache would leave that VRAM unused, I get about a third of my usual token rate.
For some definition of "typical", sure, but it depends on what performance trade someone is willing to accept. Strata LLM is geared around streaming weights as needed. It lets me run DeepSeek-4.1-Flash 1031, heavily quantized (ISTA-DASLab's 2.25 bpw quant), on a single Radeon R9700 plus 256 GB host RAM. 500 tps prefill and 30 tps decode isn't great, but it's a large fraction of what I get with Qwen3.8-27B quantized to fit entirely in VRAM for short prompts, it's faster past about 80k context, and DS4.1F can use a 1M-token context. It takes forever to fill that, or even to refill if the KV cache doesn't match, but it works.
And no wonder really - PCIe 4.0 x16 has a one direction bandwidth of around 31.5GB/s - much slower than system RAM - in my case about a third the speed and I think it's no coincidence that I'm getting this exact rate of slowdown.
Times are changing and with MTP and DFLASH, dense models are back in style. For qwen 3.8 27b, you can get great token generation rates with MTP and for quality, it's much better than a MoE of comparable size.
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-...
When asked to call 'submit_html' tool for our bench it wrote the whole page as plain reply text, then called submit_html with "This is the complete submit trigger." 4/8 tools failed.
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?
However, the chart comparing the score-by-parameters with existing models is misleading, because the others are all MoE, and this model is not. The article describes it as an architectural decision to optimize against NVIDIA cards(which sounds reasonable, as US labs are relatively easy to gain access to these high-performance chips) but the chart should've been adjusted to reflect the difference, say, by using the heuristic that `effective param size ≈ sqrt(total param size * activiated param size` on MoEs.
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.
I cannot evaluate how valuable this model is based on the performance table alone because the comparison is kind of narrow. Waiting for third-party benchmarks to provide more information.
I do not have the hardware to run it properly without super-heavy quantization (about q2). Besides, the architecture is not optimized for local usage (sliding window attention also requires KV cache). I think I will just move on.
I am looking at the HF model page https://huggingface.co/ricursiveAI/Ripple-65B-Preview and it looks like I need to wait a day for an Apple Silicon version.
Ripple-65B preview is apache-2.0, so actually open weight.
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?
I understand why some people want to differentiate between models from chinese companies and us companies and eu companies etc, because that is about control over the company. The us or chinese gov could, for example, make it difficult for canadians using a model under their control. Even with open weight, these could be weaponized to some degree. Arguably that gov control can also manifest in the model's behaviour, but since I'm mostly using them for agentic coding not political advice I don't really care about that.
But the race of the workers making this stuff... I'm not less concerned about a model made by americans working for a chinese lab than a model made by chinese people working for a chinese lab. If the race of the people building the model matters to you, why?