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

Will 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/2086756152034066792https://xcancel.com/alexandr_wang/status/2086756152034066792 reply: 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.

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 carnage. reply: 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 and thttpd (and we tested one other whose name I can’t recall).Non-forking httpd servers using select() were a popular little coding challenge for a while in the 90s....

https://xcancel.com/finkd/status/2086755195535413696"... Soon we'll also release the weights for Muse Spark 1.2, our latest foundation model..."This is bigger news - good for self hosting enthusiasts and a strategically sound move for Meta. Any push towards 'anti Chinese' models will directly benefit Meta as the competition on the frontier open-weights American models is almost non-existent. Meta will have no problem being #1. reply: Yeah, but if it's huge, how many can run it? Many folks struggle to run 200B+ models

> Muse Glimmer is a 30-billion-parameter model optimized for always-on local agent workflows. It’s small enough to run on a Mac or PC with a single consumer GPU, enabling use cases that range from local agents and function calling, to local coding, and LLM-as-a-judge evaluation.The next iteration in LLM products is a 24/7 thinking loop where the claude-code like thing gets input continuously from your wearable, notifications, and newsfeeds and is constantly preparing things for you. reply: This is is already possible with Claude Code. I use a setup where I have one instance monitoring a local queue, I have a web app for receiving webhooks from various sources and pushing them to the queue. Plus email for things that don't have webhooks. That instance then decides what to do with each input, sometimes it can spawn additional agent to investigate/prepare, sometimes it creates a ticket assigned to me and then waits for me input. All of that just uses the monitoring tools built into CC. The dispatcher loop doesn't need to be extremely smart, so I might experiment replacing it with a local model like this.

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 task and then go walk outside or do Qi Gong exercises for a while). reply: Friends Don't Let Friends Use Ollama https://news.ycombinator.com/item?id=47788385

Unsloth has quantized versions uploaded: https://huggingface.co/unsloth/Muse-Glimmer-30B-GGUFThe quantized releases often change in the weeks following release as new improvements are discovered, so either use a tool that checks HuggingFace for new versions or manually check back in a few days or weeks to check for improved versions.Initial reports are good. It hasn't been out long enough for anyone to really test thoroughly, but the people I know who have stable non-public test cases are reporting impressive results compared to even Qwen3.6 27B. That's a good sign that this might not be benchmaxxed (trained to excel at public benchmarks with less impressive performance on general tasks) which has been becoming common with recent releases.www.reddit.com/r/localllama is a good place to keep up with the details from people who are actually using it. It feels strange to recommend a subreddit over Hacker News, but on this topic the /r/localllama threads are much more on topic right now if you're looking for information about the model.There are some initial reports that even the 2-bit quantization is looking somewhat usable. That might make it small enough to squeeze into 16GB GPUs....

The gguf is up and works, I don’t know if it’s them or unsloth that’s facilitated this but it’s nice because e.g. Inkling still doesn’t appear to have support in llama.cpp which makes it irrelevant to a class of user.Unfortunately I don’t have enough experience with Qwen 27B to immediately compare, but I do it’s Qwen 3.6 35B A3. It’s much slower obviously but it seems to be way more efficient with its thinking to the point that using it might actually be faster. I find Qwen and some others rehash the same things over and over when thinking without getting anywhere, in mg limited checks here Muse is much better. reply: There's an inkling branch, go to unsloth, read - https://unsloth.ai/docs/models/inkling

With the business model for API based LLMs looking iffy at best it seems like we’re heading back to the “server under your desk” era of IT again. reply: Considering how all the big players are playing fast [1] and loose [2] with limits, billing [3] and adding undisclosed changes that burn your tokens on autopilot [4], it can't happen soon enough.[1]: Limits may change without notice, including due to capacity constraints. - https://support.google.com/gemini/answer/16275805?sjid=14713....[2]: "standard limits" are never defined - https://support.google.com/gemini/answer/16275805?sjid=14713...[3]: https://tobyonfitnesstech.com/blog/anthropic-refund-scam/[4]: https://news.ycombinator.com/item?id=48947776

Some interesting findings from the chat template designs:1. The template name is Onyx ATEM as found in the tool call exception message2. It appears to be following a harmony-style chat template. But the tool use seems to be a xml like : / / 3. atem: a internal joke of meta in reverse?https://huggingface.co/meta-models/Muse-Glimmer-30B/blob/mai...

It is interesting but it does look like a careful distillation of (Spark and) biggers open-weight models.The progress compared to Qwen3.6 27B is good, not that impressive, it's a 4 months old model. (kuto to them to compare to 27B dense and not 35B MoE, it's more fair to do so). It is very probable that Qwen3.8 27B will crush Glimmer-30B on most benchmarks.

What I think would be perfect is a model that could run on a single DGX spark and be competitive with DSV4 Flash 731. Flash is already a game changer. Hopefully meta plans on this, like the old 70b. V4 flash is smart enough for any use but slightly too big. 27b-30b isn’t intelligent enough.

Still needs 32-64GB memory to run it locally. 64GB Macbook pro with an M5 chip costs more than 4k Euros in Germany. A more practical model would be a language specific (e.g Python or JVM language) and excellent at tool calling and reasoning. Maybe that way they can shrink it even more.

I lament the comments saying this in any way redeems Meta (the company).The researchers releasing this stuff have almost nothing to do with Meta other than being bankrolled by the slaughterhouse.You aren't the customer, you are the pawn in big tech's game of thrones. Your good will is a commodity to be traded, almost literally. It will be used against you the moment it's convenient. This is open weights because Meta couldn't monetize it in any other way than to cloud developer's judgement of their reputation.But I guess most people just don't care.I'm glad it's open. It does not make me think any better of Meta.

Happy to see meta back in the game, it's like after llama nothing came out that was comparable to mainstream open models.

The post suggests that you need an rtx 5090 use it, which is currently selling for around $5,000 USD. I wouldn't exactly call that "my device", since my device costs about 25% of that for the entire computer.For the same cost, you could run on a frontier model on a pro plan for two years. The economics dont make a lot of sense for this to me, so I would love some input on why people want to do this instead (privacy, for fun, etc).

Having played around with this model a bit, I am fairly confident that it is not competing in the coding space.It can do that, but its actual selling point appears to be a different take on guardrails and safety alignment.Either that or the only new training data left was industrial quantities of dark romance literature and Wattpad.Clever business move. 131k context is more than enough for that use case, and due to that small K/V footprint, you can probably have a bunch of characters on the same GPU.Or it's just a happy little accident. We will never know.___I was informed that normal people use LLMs for mundane tasks like asking for a pancake recipie.That it apparently can also do decently.Unfortunately, it is also very confident, regardless of whether it is actually correct.So maybe it should actually stay the smut engine and nothing else.

Sorry I dont know if this is the right place but... 2000AD The Glimmer Rats , was the best drawn comic strip story by far in that publication.

Great to see Meta back, looks like really strong, local model, can't wait for llama.cpp support.

The comparison set is Gemma4-31B and Qwen3.6-27B, not the current QwenFair on size, but the headline numbers are against a model a generation back

Optimizing speed is really the way to go. Yet 24GB is not what everyone can afford. Maybe we could take some of those 56tk/s and transfer into some free RAM space using MoE loading ? I'd be glad with a less than 10GB and more than 6tk/s model.

I tried to run it with lemonade by installing it via hf but did not succeed, it gets some weird 500 errors. I also see that ollama has currently only an mlx version available.Anybody here succeed to run this on AMD?

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.net/post/3mseqv5z4qk2... )

good to see new open weights releases from meta

Let’s give thanks to all those meta engineers who have been ripped for my heir teams (while sitting right by them) working on manually tagging data. I guess the morale dip paid off in some way? I wish you all well and hope you find some happiness … IYKYK

PSA: Fast RAM isn't going to be getting cheaper anytime soon. Acquiring inference hardware is a really good way to own an appreciating hard asset. Learning how to use it and cool it is a hacker's journey worth taking. My 4090 I bought in late 2022 for $1600 is selling for a cool $3,489.95 right now, and going strong under nominal use. My DRR5 has tripled in value, my nvmes almost doubled. I grabbed a 128GB M5 Max MacBook Pro when they were still available and told all my friends to buy at least one. With that and a base M4 Studio 36GB, HuggingFace rates that hardware as:> Amazing! You have a total of 128.94 TFLOPS of computing power. 71.3% percentile on scale of "GPU Poor" to "GPU Rich"The way I see it, these are amazing machines that the richest folks are hovering up. I think they should be in the hands of regular people as much as possible. They depend on an incredibly global, increasingly fragile supply chain. If the become impossible to produce, their value would increase tremendously. I think they will become really valuable to you to use the tokens directly, but if that isn't the case, they can be rented out or resold. Please don't just buy any hold....

Meta seems like the one American bigtech that would distill the the other American frontier models. My enemy’s enemy is my friend?

Looking forward to giving this a try with llama.cpp. I’m watching the open-weights competition with high expectations.

I'd really like to see a 45B-ish dense model ready for a dual GPU setup. Something with a little more intelligence while still within the range of some higher end local setups.