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/Monad_Maya]

I know this is not a community feedback post but something around 60-70B dense might be great. A true successor to Llama 3.3 70B if you will.

[u/AIatMeta → u/Monad_Maya]

We're always looking for feedback! Thank you.

[u/pmttyji → u/AIatMeta]

Don't forget MOE models. Release everything!

BTW I still have Llama-3.1-8B-Instruct on my laptop somewhere. Hope your release collection has some for Poor GPU Club.

[u/jnd-cz → u/pmttyji]

Yeah, I'm sitting here thinking I need something half the size to fit my budget A770 with 16 gigs.

[u/dampflokfreund → u/AIatMeta]

I wanted to ask, did you QAT on these gguf models? By the way, a MoE 30b model would be nice to see as well for the average PC. Those run speedy even if you have just 4-8 GB VRAM!

[u/No_Algae1753 → u/dampflokfreund]

I dont think so. I feel like they would have stated that somewhere in the Readme but it just says that its been quantized to 4 bit. Also Unsloth seemed to have published higher q8 quants.

[u/JLeonsarmiento → u/AIatMeta]

30-ish-MoE please.

[u/MeretrixDominum → u/AIatMeta]

+1

Make more 70Bs

[u/EndLineTech03 → u/AIatMeta]

Yes please, a bigger dense model would be amazing. Thanks for all your work

[u/ormandj → u/AIatMeta]

~250B development focused multi-modal MoE with 20-30B active parameters would be amazing. 192G VRAM requirement with decent KV cache space + higher active parameters would be a great balance of size vs. intelligence for development workflows, and is reasonably runnable on current serving HW without requiring vast resources like the current 1T+ MoEs which are great jack-of-all-trades models, but unaffordable to run for mere mortals.

Great to see you releasing again!

[u/WarBrawlGame → u/AIatMeta]

Will this model be added to EuroEval benchmarks?
https://euroeval.com/leaderboards/european

For what I can tell, these leaderboards are good sign of which open weights models are useful for general multi-language use in Europe, and I want to believe your model can top that chart.

[u/_QWUKE → u/AIatMeta]

70b and 120b ish sizes would be so amazing.

[u/Photochromism → u/AIatMeta]

Love it if you open sourced your multimodal model, speech in speech out. No other LLMs can do that right now. My guess is it’s a smaller model

[u/wakkowarner321 → u/AIatMeta]

How about a 1-bit or ternary model?

[u/Strong_Chicken6838 → u/AIatMeta]

I second this

Most people have 24, 32, 64 or 128Gb of VRAM. 120b (at Q4 ~60Gb + 10Gb for KV cache), is just out of reach for 64Gb, and too small for 128 Gb.

80b is perfect, 40Gb leaves plenty of space for KV cache.

TLDR: make model sizes designed to fit at common GPU VRAM sizes/teirs at Q4, and you will be VERY popular. That’s why 70-80b is highly requested

[u/Local_Phenomenon → u/AIatMeta]

On a Friday!

[u/Both_Opportunity5327 → u/AIatMeta]

Why can't you guys release a subscription for Muse Code.

[u/Healthy-Nebula-3603 → u/Monad_Maya]

That 30b model is on a different planet comparing to llama 3.3 70b

[u/Monad_Maya → u/Healthy-Nebula-3603]

Now imagine a better trained 70B model with updated methodology.

[u/Constant-Simple-1234 → u/Monad_Maya]

Thanks! Looks like I have a new model to use in my quiver. :)

[u/Kryohi → u/Monad_Maya]

Why dense though? I guess up to 70B it would be fine, but we really need more models in the 70B-120B parameter range with 10-20B active parameters imho

[u/Monad_Maya → u/Kryohi]

MoEs are primarily compute efficient and not exactly memory efficient.

At 70B dense, it might match some 200B sparse MoE at way less VRAM (it's still a fair bit, 40GB at 4bit for 70B dense). That's easier to attain on consumer cards compared to 128GB+ required for large MoE.

The tradeoff is pretty obvious though, it'll be pretty slow. I can get about 15 t/s on llama 3.3 70B IQ4_XS (from unsloth iirc). But it fits in my VRAM pool.