Hacker Times

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

Discussion

I don't think that's entirely true. As an example you can look at how the European Union is increasingly putting up trade barriers with China and identifying opportunities for new rules and regulations to prevent Chinese dumping as America is also trying to do.

Japan, South Korea, the Philippines, and other countries participate in freedom of navigation and combined arms exercises because they perceive China to be a threat to their countries. [2]

I think it's more of a mixed bag. You see a lot of public talking points, and in Europe specifically a lot more healthy discussion about not being militarily as dependent on the United States as it has in the past and looking itself to follow Trump's lead to onshore capabilities (cloud for example) but I wouldn't read such moves as moving closer to China so much as they are hedging their bets a bit more.

Here's an article [1] that was reporting on this topic that I found interesting along with a select quote from the article:

  > Luxembourg’s Prime Minister Luc Frieden said Thursday at an EU summit that China is “an existential threat for our industries.”

  > Even Germany, whose economy has long relied on exports to China, is alarmed. Chancellor Friedrich Merz said this week that Beijing keeps its currency up to 30% undervalued, calling this “a massive competitive disadvantage.”

[1] https://www.wsj.com/economy/trade/chinese-export-flood-tests...

[2] https://www.msn.com/en-us/news/world/u-s-and-9-allies-just-h... - this isn't the "best" article but I just grabbed one to illustrate the point.

Today, we're introducing Muse Glimmer, the next model from Meta Superintelligence Labs, and open sourcing the model weights under a permissive Apache 2.0 license.

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. Muse Glimmer delivers strong performance on key agentic use cases and benchmarks compared with leading models in its size category.

Foundation models have achieved remarkable capabilities across reasoning, code generation, and tool use — yet most deployments still depend on cloud infrastructure and network access. Running models locally enables you to use AI anywhere, anytime, with or without an internet connection. This is increasingly viable: the open source community has shown that smaller models, when trained effectively, can approach frontier-level performance on targeted tasks. Muse Glimmer is optimized for these local use cases.

Keeping with our long tradition of sharing fundamental AI research, we're releasing Muse Glimmer open weights today on Hugging Face, along with developer documentation to help you start building and running your own agents. Muse Glimmer is built to work with the tools developers already use. Optimized integrations on llama.cpp, MLX, and ExecuTorch will land in the coming days, so you can go from download to working agent in minutes.

How We Trained Muse Glimmer

An agent that manages your schedule, drafts your messages, organizes your files, and learns how you work needs deep access to personal context. It also needs several capabilities working in concert: long-horizon execution, precise tool calling, multimodal understanding, long-context memory, and instruction following.

We designed Muse Glimmer to balance capability against the memory and compute constraints of local hardware. This required a compact architecture, a novel distillation recipe that transfers agentic reasoning from a much larger teacher model, and inference optimizations — including quantization — to meet latency expectations. We achieved this in the following phases:

Pre-Training. We trained Muse Glimmer on Muse Spark's outputs using logit distillation, leveraging a similar data mix as the teacher.
Mid-Training. We trained the model on longer-context, more agent-heavy data with richer reasoning traces, alongside organic data.
Post-Training. We combined supervised fine-tuning with a mix of on-policy distillation and reinforcement learning across general, reasoning, coding, and agentic domains.

Muse Glimmer was evaluated under the standards set out in Meta's Advanced AI Scaling Framework and assessed for open-weight release across all relevant categories.

Built for Agents: What Muse Glimmer Can Do

Building effective agents requires key capabilities working together to achieve the user’s goals. Muse Glimmer is trained and evaluated across each of the following:

End-to-end Agentic Task Completion. Muse Glimmer achieves strong success rates on full-task benchmarks including DeepSearch QA, MCP-Atlas, 𝛕-Bench and SWE-Bench, which measure its ability to work within scaffolds, write and debug code, and resolve multi-turn requests from start to finish.
Reliable Tool Use. The model handles a wide range of function calls, invoking tools with precise schemas throughout extended workflows.
Multi-Step Reasoning. Muse Glimmer chains reasoning over long horizons, sustaining coherent plans across complex, extended workflows.
Failure Recovery. When a tool call fails or returns an unexpected result, the model is trained to diagnose the error and retry rather than halt.
Multimodal Input and Reasoning. Through a dedicated perception encoder, the model accepts interleaved text and images. This enables agents to interpret screenshots, charts, and documents alongside conversation.
Scaffold Compatibility. Muse Glimmer works across OpenClaw and other agentic orchestration patterns.
Controllable Effort. Muse Glimmer supports different reasoning strengths to select the right balance between quality and speed.
Multilingual. Muse Glimmer is trained on data from more than 100 languages.

Performance

We evaluated Muse Glimmer across a broad range of benchmarks to assess the diverse capabilities required for effective autonomous agent behavior. Compared with Gemma4-31B and Qwen3.6-27B, Muse Glimmer performs strongly for its size class on several widely used LLM benchmarks.

Table comparing Muse Glimmer 30B with Gemma4 31B and Qwen3.6 27B across agentic, coding, multimodal, safety, and reasoning benchmarks.

For more detail about our evaluations, see our report.

Optimized for Local Deployments

A local agent is truly useful if it's fast enough to feel responsive. An agent that takes minutes to reply or plan its next step breaks the flow of real work. We applied two optimizations to make Muse Glimmer run at practical speeds on consumer hardware without sacrificing quality.

Fitting the Model on Your Device.

At full precision, a 30-billion parameter model would require over 55 GB of memory — far more than any consumer GPU offers. We use quantization techniques to compress the model's weights to approximately 4-bit precision, shrinking the language model to under 20 GB. This leaves enough headroom for the model's working memory (its "KV cache"), the perception encoder for image understanding, and the speculative decoding drafter to run simultaneously within a 24 GB or 32 GB envelope. We validated that this compression introduces minimal to no degradation on agentic tasks.

Table comparing full precision, K-Quant-Dynamic, and K-Quant-17GB by accuracy degradation and target hardware memory.

Faster Generation Through Speculative Decoding.

Language models normally generate text one token at a time, which can feel slow during long reasoning chains or multi-step tool calls. Muse Glimmer ships with a lightweight "drafter" model based on DFlash — a small companion network that proposes entire blocks of tokens at once. The main model then verifies these proposals in parallel, accepting correct tokens and correcting wrong ones. This technique lets Muse Glimmer generate text significantly faster than standard token-by-token generation while producing identical output quality. We provide quantized drafter versions to incur a smaller memory overhead in the release.

The Result:

We measure the speed of our K-Quant-17GB model alongside the quantized DFlash drafter on MacBook M4-Max, M5-Max and on a RTX-5090. The model is fast enough for fluid conversation and real-time agent interaction, all running entirely on your device.

Bar chart showing DFlash speculative decoding increasing Muse Glimmer decode speed by 3.1 times on RTX 5090, 1.8 times on M5 Max, and 1.5 times on M4 Max.

Get Started With Muse Glimmer Today

Muse Glimmer is available now, and you can download the weights on Hugging Face. In the coming days, run it locally through partners like Ollama, LM Studio, and Unsloth, deploy it with edge frameworks including llama.cpp, ExecuTorch, and MLX, serve it at scale with vLLM and SGLang, or get started quickly through partners like Together AI, Fireworks AI, and OpenRouter. You can even customize it for your use case by leveraging PyTorch’s TorchTitan training feature to tune the model further.

We're also working with our partners including AMD, Arm, Dell, Intel, and NVIDIA to optimize performance across devices. In addition, we’re releasing documentation so developers have the resources they need to get started and build responsibly with Muse Glimmer. This includes guidance on setting up custom scaffolds, so it's even easier to start building and deploying personal agents on day one. You can learn more and find resources to build on Meta's AI Developer Center.

This work builds on Meta's long track record of open AI research, extending it into agentic AI and giving developers access to local agentic capabilities. As always, we welcome feedback from the community and can’t wait to see what developers build with this open weights model.

Download the Model on Hugging Face Developer Documentation

jermaustin1•about 17 hours ago

Somewhat hand rolled, somewhat claude coded.

Back in 2023 I started my own C# LLM library for doing tool calls and structured output, and over the years it has morphed bigger and bigger, and that is the backbone of almost all of my LLM-based projects.

I've never released it, but its easy to understand, and simple to add your own tools:

  [AIDescription("Get current weather for a location")]
  static string GetWeather(
    [AIDescription("The city name")] string city,
    [AIDescription("The country name")] string country,
    [AIDescription("Temperature unit", ["C", "F"])] string unit = "C")
  {
    // make some API call to a weather API and return a string to the LLM
    return $"The weather in {city}, {country} is 22°{unit} and sunny";
  }
  
  var chat = client.StartConversation("You are a helpful assistant with access to weather data.");
  var response = await chat.SendAsync<string>("What's the weather in London?", GetWeather);

I'm sure plenty of better libraries exist for this now, but in 2023, I don't think any existed in the dotnet ecosystem. I've never released it though, because I've never "finished" it.

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keeda•about 8 hours ago

If I understand correctly, you're saying people are compute-poor but not necessarily GPU-poor because there's a lot of GPUs out there but nowhere to plug them into? If so, I'm not sure that distinction matters to the GP's point that there is too much demand to call this an oversupply.

I also doubt we can estimate the level of demand based on a single deal between Anthropic and SpaceX (despite which, note, Claude still stuggles at times.) Consider other signals, like Google, who we thought had an insurmountable infra advantage, also renting compute capacity from SpaceX and limiting Meta's usage (along with other clients apparently) to conserve capacity: https://www.cnbc.com/2026/06/28/google-limits-metas-use-of-i...

I am not sure Microsoft thinks there will be an oversupply either; last earnings they announced bumping up their CapEx spend, along with all the other hyperscalers.

Here's a way to estimate how much room there is for demand to grow. Various sources (linked in this comment, along with more analysis: https://news.ycombinator.com/item?id=49089296) indicate that even though a large number of people (50 - 60%) are now using AI at work, they use it for only 6% of their work hours.

That means, even if AI can only address 30% of all work, there is still 5x potential demand growth left! Note, the sources above indicate that AI is even being used in non-knowledge work industries, so the scope is already larger than we thought. This is in addition to the remaining 40 - 50% of people are still not using AI at work. Plus we know that agentic workloads consume way more tokens, so that's yet another multiplier.

But will that demand keep growing? Well, some of those same sources above mention that most executives are planning on ramping up their AI spend in coming years.

Putting all this together explains the hyperscalers' quarterly bemoaning of how strapped for compute they are and why they are spending so much to add more capacity. Given this, an oversupply seems pretty unlikely.

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ericmay•about 18 hours ago

> I am very pro the "dream" of America, in terms of liberty, democracy, etc. According to every "democracy index" I'm aware of we're not doing so hot in that regard, generally rating as a flawed/deficient democracy and the trends are going in the wrong direction, fast.

Well, to be fair people do have different dreams. I'm not sure those indices count for a whole lot. As an example, folks who argue in favor of returning more power to the individual states are, certainly, acting in accordance with stronger democratic principles. I'd argue the EU is actually a bit anti-democratic as it removes more power from local populations and individual states/countries/entities. Yet how would individual democracy indices rate these two?

And if you disagree with my perspective on what is more democratic, well, who is right and who is wrong?

> Generally, no, but this is way too open-ended of a question. I want good economic opportunity for everyone, including every US citizen. But relevant to the OP if Meta got snapped out of existence I think it would be a net positive for the world.

I'm no fan of Meta. But they aren't the only entity where if they snapped out of existence it would be a net positive for the world. I can think of a few non-corporate entities at least.

But fair enough it's a bit open-ended. I guess if push comes to shove when you say you are anti-American do you want to see, for example, economic opportunity increase in other countries at the expense of Americans? Not all relations have to be such give-and-take, but certainly many economic opportunities are. I'm just trying to learn more about what being an anti-american means to you.

> Nope. But across our entire history ask yourself how many foreign countries have attacked the US? Now ask yourself how many the US has attacked. With those numbers in mind, does the US seem like "the good guys"? really? ...really?

I'm fine doing things like removing all of our overseas bases, leaving alliances including NATO, never again being involved in the affairs of other countries, whether that's Korea (Korean War), Kuwait, Bosnia, or Iran, or Taiwan and China, and cutting our military spending quite a bit to reflect your desire to not be the bad guys anymore. I say let's just sit back and let the rest of the world figure out their problems - why do we need to be involved?

Is that something you agree with as well? Would that be anti-American in alignment with your views or is there other nuance here I'm not properly capturing?

> Nope, but I'm not cheering for them either just because they are American, I'm not a tribalist.

And to be clear even if America was even, say, your ideal dream since you're not a tribalist you wouldn't cheer on American athletes and would also label (not as a matter of insult but as fact) other people who cheer on their compatriots as tribalist?

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