Random question: Would you be able to run this model on a Macbook Air M5 (latest)?
It it has less than 64gb then probably not
Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows
501–510 of 682 posts
Re: Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows
#502Earlier quoted context omitted.
US companies published their research on transformers. The only reason Chinese companies have any models at all is open US research... Add distillation to that, and the entire Chinese AI sphere is really just an offshoot of public US AI information...
Science doesn't work this way, someone could claim only reason World is successful because Chinese invented paper, so please don't go there. Regarding distillation, you can also say Anthropic and OpenAI stole /distilled books, articles, blogs from everyone who published things without knowing their work might be used against them
> Science doesn't work this way, someone could claim only reason World is successful because Chinese invented paper, so please don't go there.
Perfectly valid claim. It could be debated though, since you are talking about a centuries long diaspora of processes.
I'm talking about an architecture that was invented, published, and pioneered by US companies just a few years ago. Not really the same argument IMO.
> Regarding distillation, you can also say Anthropic and OpenAI stole /distilled books, articles, blogs from everyone who published things without knowing their work might be used against them
Yes. I would argue that anthropic et al are only able to exist because of the massive body of work in the anglosphere, both in books and online. That goes hand-in-hand with my view of the Chinese AI sphere.
Re: Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows
#503Earlier quoted context omitted.
Yes, and also waiting for the next iteration of Gemma. Muse or Qwen are optimized for coding, while IMO Gemma is still better for non-coding tasks. https://x.com/osanseviero/status/2086107547535122767
You can partially tell by the tokeniser; which gives you some hint into the training corpus mix. is four Gemma4 tokens, but one Qwen3.6 token.
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Re: Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows
#504Earlier quoted context omitted.
What specific technical signals make you think we're close to a shift like that?
The researchers who published Attention Is All You Need didn’t have the benefit of the LLMs they birthed. Take a look at the prompt that solved the Cycle Double Cover conjecture, and which has been adapted to achieve breakthroughs in cybersecurity. The field is entering a feedback loop that is leading to exponential innovation. We’re at the beginning of the curve. And right now the big iron data center approach is br…
I dont think its exponential innovation. Rapid incremental innovation is happening very fast with some occasional bigger bumps.
Re: Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows
#505Earlier quoted context omitted.
What specific technical signals make you think we're close to a shift like that?
The researchers who published Attention Is All You Need didn’t have the benefit of the LLMs they birthed. Take a look at the prompt that solved the Cycle Double Cover conjecture, and which has been adapted to achieve breakthroughs in cybersecurity. The field is entering a feedback loop that is leading to exponential innovation. We’re at the beginning of the curve. And right now the big iron data center approach is br…
Because I wasn't familiar with it and others might be curious too: that prompt is available at https://cdn.openai.com/pdf/04d1d1e4-bc75-476a-97cf-49055cd98... and is just below 5 KiB of text.
Re: Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows
#506Earlier quoted context omitted.
I’m sure you could find this exact same comment regarding technology in 1999.
You can find it today for gaming. Even despite the outlandish rise in hardware costs, there is very little demand for cloud gaming.
Yeah but I presume this has more to do with latency than overall compute, right?
Re: Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows
#507Will be interesting to see how Qwen3.8 27B compares against this once it releases this week. Seems like dense 30B is back in fashion? EDIT: An open weight version of Muse Spark 1.2 is going to be released as well: https://x.com/alexandr_wang/status/2086756152034066792 https://xcancel.com/alexandr_wang/status/2086756152034066792
> Seems like dense 30B is back in fashion? Huh, well... no? Gemma A4B and Qwen A3B are quite popular in fact. I'm sure 3.8 35B A3B will outperform 3.6 27B by all metrics
One thing I would caution is staying out of the prediction market like this.
Tech tends to get boring when you judge current products against the hypothetical capabilities of unannounced products that may never ship. It's like comparing Nikon cameras against Canon camera rumours, or comparing iPhones against unannounced and therefore largely imaginary Samsungs.
- If they do a Qwen 3.8 35B A3B (and I hope they do because I love the 3.6 version)
- and if it beats 3.6 27B by all metrics
… then the local open weights world will be a better place.
But they have said nothing about it and they dropped several weight classes for 3.6, so who is to say they won't drop the 35B? And even if they don't, this is a tall order; why would the MoE tradeoffs no longer be apparent? (Again, I really like both the Qwen and Gemma MoEs)
FWIW I am enjoying testing Muse Glimmer — it's really quite impressive on chat, has nice terse and even amusing thinking traces, a bit of brass to it, and I'm hoping it will be good on agentic stuff.
Re: Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows
#508Re: Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows
#509Earlier quoted context omitted.
It's not perfect but it is very terse! Better than BottleCap managed to do with post-training Qwen in ThinkingCap. I suspect it will help a lot with enabling preserve-reasoning, because the biggest apparent limitation of this model is the 128K context window. Though the practical issue I am seeing on my M1 Max MBP is that performance suddenly drops off a cliff if I have DFlash enabled.
128k context window is a complete non-started for us. We need to optimize our most needy agentic jobs, but our average context is well above that
The model card does say 131K+ but I have no idea what scope the + really has in practice. Feels like overclocking; you're-on-your-own territory?
One thing I will say is that its thinking traces are really quite terse. It genuinely seems to spend many fewer tokens on reasoning. So that might help a bit.
Re: Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows
#510Earlier quoted context omitted.
Do AI companies make release plans based on upcoming other models like this? I would think all the processes that go into the repository and weight infrastructure pre-training, checkpointing, knowledge distillation, model compression, post training pipeline, ecosystem integrations, inference API, benchmarking, human eval/safety/alignment, docs, etc... all that dictates the release schedule.
Any company working in a competitive industry is generally aware of what their competitors are doing. PR is an important aspect to market success, so it factors into release schedule. It may not be the dominant factor given engineering constraints, but yea, it’s certainly a factor, and a large one at that.
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