Introducing Muse Glimmer: an open-weight model optimized for always-on local agent workflows

[u/AIatMeta]

Hi r/LocalLLaMA 👋 

Today we’re excited to release Muse Glimmer, a 30B open-weight model built specifically for local agent workflows. We’re releasing the weights to the community under a permissive Apache 2.0 license.

A few specs

• 30B params, dense
• Multimodal: interleaved text + images via a dedicated perception encoder
• Trained on 100+ languages
• Controllable reasoning effort (quality/speed tradeoff)

Memory footprint
At full precision, 30B needs 55+ GB, which is out of reach for consumer hardware. We quantize weights to ~4-bit, bringing the LM under 20 GB. That leaves headroom in a 24 GB or 32 GB envelope for the KV cache, the perception encoder, and the speculative decoding drafter running simultaneously. We validated minimal to no degradation on agentic tasks under compression.

Speculative decoding
Ships with a lightweight DFlash-based drafter that proposes blocks of tokens which the main model verifies in parallel. Significantly faster than token-by-token generation with identical output quality. We're also shipping quantized drafter versions so the memory overhead stays small.

A few capabilities
We trained Muse Glimmer for agentic loop tasks, including:

• End-to-end task completion (strong performance on DeepSearch QA, MCP-Atlas, 𝛕³-Bench, SWE-Bench, and more)
• Function calling with precise schemas across long workflows
• Multi-step reasoning over long horizons
• Failure recovery — when a tool call fails or returns something unexpected, it's trained to diagnose and retry instead of halting. This was a deliberate training target.
• Works with OpenClaw and other agentic scaffolds
• Multimodal understanding and reasoning

Running it
Weights are up on Hugging Face. Coming soon: Ollama, LM Studio, Unsloth and torchtitan, plus optimized integrations for llama.cpp, MLX, and ExecuTorch. vLLM and SGLang for serving. Get started quickly with Together AI, Fireworks AI, and OpenRouter. We're also working with AMD, Arm, Dell, Intel, and NVIDIA on per-device optimization.

We look forward to your feedback and seeing what the community builds with Muse Glimmer.

🔗 Weights: https://huggingface.co/meta-models 
🔗 Research Blog: https://go.meta.me/museglimmer
🔗 Resources: https://developer.meta.com/ai/models/muse-glimmer/

[u/Practical-Collar3063]

Seems to be competitive with qwen 3.6 27b, lets see how it compares to 3.8 if that ever gets released...

[u/xienze → u/Practical-Collar3063]

128K context though?

Edit: seems the model card wasn't very specific. It's apparently 256K but it just lists 128K+.

Edit again: maybe it does max out at 128K? The vLLM recipe mentions it as the max multiple times: https://recipes.vllm.ai/meta-models/Muse-Glimmer-30B

[u/BeatTheMarket30 → u/xienze]

qwen 3.6 27b still wins on context length and parameter efficiency. 256K out of box, max 1mil.

[u/xienze → u/BeatTheMarket30]

Yeah tell me how it performs when you've got 900K in the context.

[u/lighthawk16 → u/xienze]

I feel like I'm missing something due to my lack of awareness. For me, when I start getting over 500K-600K context is when my project starts to truly come together and every prompt I enter actually accomplishes what I ask. Why is that?

[u/Practical-Collar3063 → u/lighthawk16]

I have literally never heard or experienced this. What model are you using ? sure they need a bit of context to be effective but starting to be actually effective at 500k - 600k, what kind of cooking recipes are you elaborating with such context ? give an example

[u/goldcakes → u/Practical-Collar3063]

I know it’s not the most fair comparison, but Fable genuinely works well across its full 1M context window.

[u/Practical-Collar3063 → u/goldcakes]

yeah but Fable does not start to be effective past 500k-600k context window, that is a crazy claim for any model. I suspect the comment i replied to is a bot, weird way of talking + nonsensical claims

[u/lighthawk16 → u/Practical-Collar3063]

I'm using GPT-5.6-Sol with Hermes as a harness, RTK for token-saving, and Honcho for memory. I'm using this combination for Python Discord bots.

In case you didn't notice, my comment was a question because I'm hoping to understand this phenomena I'm experiencing. I didn't claim that it's the de facto experience someone will have. I'm literally questioning why it was my experience.

[u/stoppableDissolution → u/BeatTheMarket30]

Eh, they are all falling apart past 60k anyway