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Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows

research.meta.ai

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Re: Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows

#521

Post by cynicalsecurity »

brandon272 wrote: post #208
cynicalsecurity wrote: post #69

Earlier quoted context omitted.

I don't understand the desire to run own AI models for programming locally. No laptop is ever going to be as powerful and energy efficient to run anything close to OpenAI, Anthropic or Google models. A model you can run on a loptop is simply not going to work as well as it's needed for programming. Small models for linguistic work fine, but anything more sophisticated simply won't provide enough resources or power. O…

> I don't understand the desire to run own AI models for programming locally. Privacy. Security. Not bulk uploading your trade secrets and intellectual property to Sam and Dario’s servers.

I was suggesting running personal AI servers.

Re: Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows

#522

Post by gkd6 »

mmaunder wrote: post #249

Remember when we needed 200 servers for an enterprise website because Apache used one process or thread per connection - and Nginx collapsed that into a single box overnight? That moment for LLMs is near. It’s going to move us from the big iron era of AI to small portable brains. Nature has already proved it’s possible with 20 watts and very little heat generation. And I think the data center buildout will end in car…

Nature takes its own sweet time to come up with photosynthesis or the krebs cycle. What takes 2 billion years for Nature to work out, these large systems will soon do it in 2. They have capacity to compress time in ways the chimp troupe cant.

Re: Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows

#524

Post by LeBit »

cube00 wrote: post #454
mark_l_watson wrote: post #284

Meta is rocking AI. As of last week I have been using their excellent muse coding harness with their model Muse Spark 1.2. Starting this morning I am running their new local 30B model muse-glimmer on my old MacMini 32G using Ollama (remember to increase the context size!) and pi coding harness. I am getting good results with muse-glimmer running locally, with the caveat that everything runs slowly (e.g., give it a ta…

Friends Don't Let Friends Use Ollama https://news.ycombinator.com/item?id=47788385

When that article was first published , I started looking into llama.cpp. With the help of an LLM I researched the knobs to turn that made most sense.

Things went from "local models are useless unless you have a 512GB GPU I guess" to "oh.. I can do a lot of stuff locally now!"

Re: Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows

#525

Post by stymaar »

dannyw wrote: post #150
karimf wrote: post #44

Earlier 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.

Looks like we have a /r/localllama dweller here.

Re: Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows

#526

Post by LeBit »

computershit wrote: post #475
lenerdenator wrote: post #461

Earlier quoted context omitted.

What do you use instead?

I use llama.cpp w/ llama-swap https://github.com/ggml-org/llama.cpp https://github.com/mostlygeek/llama-swap

Thanks for the link to llama-swap. Didn’t know about it and will definitely install it.

Re: Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows

#527

Post by dofm »

ignitioncar wrote: post #523

[flagged]

I haven't really got that far in, but it writes in a sort of clipped, geeky note form in the reasoning traces without too obvious claudeisms, it seems to have been trained to have a level of wit, almost.

Like, in the car wash test, this was in the thinking traces:

“Walking won't get the car washed.”

and:

“Perhaps answer: Walk if you want to wash yourself? No”

Which made me laugh out loud.

Even in the final answer:

- - -

You have to drive it. Walking 50m won't get the car clean, it'll just get you to the car wash.

If you mean you going to the car wash to check prices / pay / get a brush, then yeah, just walk the 50m. It's about 30 seconds on foot and you save the cold-start emissions of firing up the engine for a distance you could roll.

If you mean the car itself getting washed, the car needs to be at the car wash. You can push it 50m for a workout, but driving it 50m is the practical way.

- - -

The emphasis on "you" was from the model. I mean I write like this so I can't judge its tone harshly :-)

ETA: The knowledge cutoff is January this year, so it didn't encounter car wash discourse in the scraped training set, though I suppose you can't rule out some kind of fine tuning to deal with this scenario. Still made me chuckle.

ETA 2: obviously I wrote this before you added your last paragraph. WTF dude.

Re: Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows

#528

Post by Matl »

onlyrealcuzzo wrote: post #519
pu_pe wrote: post #46

Earlier quoted context omitted.

Based on the benchmarks, it seems that Muse Glimmer barely edges out against Qwen3.6 27B, except for tool-calling skills (MCP, etc.). I wouldn't be surprised if they released it now because they are afraid they wouldn't beat Qwen3.8 27B.

I would hope that Qwen 3.8 is better. It's been 4 months, and we've seen almost no progress in this space. As people have called out, Glimmer appears to be a trade-off rather than a clear winner. And from what I've been reading, no one is expecting Qwen 3.8's model in this space to be a clear winner, but just slightly and marginally better. That's a little concerning as DeepSeek v4 Flash proved at it larger sizes the…

> If we don't see something that's substantially better in the ~30B param space soon - it would appear we might've saturated that size with knowledge.

I wouldn't be quite so pessimistic. We may have saturated the current approach, but I think there's a lot still left in terms of compression, attention, active parameters, caching etc. etc.

Re: Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows

#529

Post by notnullorvoid »

technocratius wrote: post #506
notnullorvoid wrote: post #487

Earlier quoted context omitted.

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?

Yes latency, and the usual preference of ownership over rentership. Similarly their are benefits to running local AI too, like data privacy and control.
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