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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

#461

Post by lenerdenator »

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

What do you use instead?

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

#462

Post by Karrot_Kream »

seizethecheese wrote: post #439
snowwrestler wrote: post #400

Earlier quoted context omitted.

> On the other hand, one should not discount the value of HN as tastemaker and trendsetter. I would encourage dedicated readers here to aggressively and persistently discount the value of HN as a tastemaker and trendsetter. HN is actually a trailing indicator on tastes and trends, essentially by design. Things only make it to the front page if they get submitted and voted upward by a large number of people. That mean…

I think you're wrong. To gut-check, I asked a panel of AI models "What platfroms should be considered tastemaker or trendsetter for adopting new AI models". Here's the part of the answer relevant to this discussion: > AI X/Twitter — a few hundred accounts effectively set the narrative in the first 24 hours; vibe checks here outrun benchmarks. > r/LocalLLaMA — the open-weights kingmaker; a model that fails here doesn'…

The internet will always be a trailing source of these things. If you have to ask the internet, you're behind. The internet is a form of information exchange; information about the thing needs to exist before it is exchanged.

For a brief moment the readership of HN heavily overlapped with founders in Silicon Valley, and for those years HN was indeed a platform that acted as a trendsetter or tastemaker. That time has long passed. Twitter is the closest to this today, which is on that that list, and makes it a decent place to listen. r/LocaLLaMa is not bad as a support forum for GGUF and quantizer projects but it's at the level of "Windows tutorial" of software from the '90s.

If anyone is interested in being at the "edge" of this, I suggest simply going to meetups in tech hubs where people are working with AI and models. My guess is any city with a major tech presence will have more "edge" than HN. I'm fortunate enough to be in Silicon Valley right now and have friends who work at foundational lab companies so it's not hard to stay on top of what's happening. The "internet" of 2026 is just much, much bigger than the internet of 2007 when HN was founded, and so it's just a lot harder to find the information that you need.

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

#463

Post by BoredomIsFun »

kolbe wrote: post #241
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.

Qwen3.6 27B is the go-to medium sized model for coding, so beating it is not a small achievement

There is a finetune Qwen3.6-27B-Fable-Fus-711-UnHeretic-NM-DAU-NEO-MAX-NEO which seems to be as good at coding as vanilla Qwen, but way, way better at creative writing than Qwen and even better than Gemma 4 26 and 31b.

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

#464

Post by BoredomIsFun »

nbardy wrote: post #343
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…

Everyone keeps repeating this who doesn’t understand the underlying technology. Small llms are still way more efficiently server on big GPUs. Sharing server capacity takes advantage of the massive parallel throughput and sharing of memory bandwidth. You are sharing the GPUs with thousands of concurrent users.

True, but local setups can run LLM requests in parallel too. In this case efficiency gap is much narrower.

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

#465

Post by notnullorvoid »

dofm wrote: post #272
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…

Side note! Nginx was by no means the first web server to use a non-forking mechanism, nor the first open source web server to do so. Certainly Zeus (which was closed source) was earlier and very useful in this sort of application, and so was thttpd (open source, still exists as Merecat). I used thttpd quite a bit for single box applications and at one of my employers, nginx replaced a mixed strategy with Zeus, Apache…

We've barely even started on optimizations like advanced language aware grammars, and specialization routing (dynamically loading fine tunes or seperate weights for specific tasks or languages).

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

#466

Post by [deleted] »

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…

[deleted]

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

#468

Post by solarkraft »

akazantsev wrote: post #167
solarkraft wrote: post #11

Wow, Meta is back (at least for now)! I like this class of model. Multi-token prediction makes it viable to run dense models at not-too-far-off speeds as MoE models with much better intelligence. The submission’s title (open weights 30B local coding model) is luckily wrong: This is meant to be a general agentic model. It even comes pre-quantized and with a MTP/drafter model. Looking good! Let’s hope they aren’t disho…

> The submission’s title (open weights 30B local coding model) is luckily wrong: This is meant to be a general agentic model. https://xcancel.com/alexandr_wang/status/2086756152034066792 It's correct. See the OpenCode demo. Generic models are good enough for coding without necessarily being designed specifically for coding.

Right, so it's as correct as me claiming it to be an E-Mail sorting model. It may be good at that, but that's not its primary purpose.

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

#469

Post by joquarky »

Arwill wrote: post #459
modzu wrote: post #294

Earlier quoted context omitted.

brains do it with 20 watts because theyre analog. llms require massive amounts of power and this isnt changing any time soon without a breakthrough

There are arguments that the brain is quantum, as in parts of it locally using quantum effects. Which if true, might make a counter-argument, as there will be bigger data centers needed if the goal is to simulate the brain classically. On the other side, advancement in quantum computers would make current LLM inference much faster. Because of the extreme cooling needed, i dont think the energy demand would become les…

> the brain is quantum

Doesn't that apply to everything that exists in the material world?

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

#470

Post by BoredomIsFun »

tarruda wrote: post #366
simonw wrote: post #357

Pelican, rendered by Muse Glimmer on my Mac running LM Studio (with this model release: https://lmstudio.ai/models/muse-glimmer ): https://tools.simonwillison.net/markdown-svg-renderer#url=ht... It has all of the components of a pelican riding a bicycle, though not exactly arranged in the right order! (For comparison, here are the pelicans I got from Muse Spark 1, 1.1, and 1.2: https://bsky.app/profile/simonwillison.…

> It has all of the components of a pelican riding a bicycle, though not exactly arranged in the right order! Maybe a sign that they didn't have SVG pelicans in the dataset

It is very bad with any svgs.
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