Muse Glimmer 30B
Muse Glimmer 30B is Meta Superintelligence Labs' open-weight multimodal model for local agents, coding, tool use, long-horizon reasoning, and image understanding.
Provider
Meta
Model family
Meta Muse
Open-weight multimodal agentic LLM
Cost tier
Glimmer 30b
Status
Current
Release Aug 10, 2026
Why teams choose it
The released model is Muse Glimmer 30B; Meta says open weights for a Muse Spark 1.2 vers…
ion are planned for the coming weeks, not available through this release.
The full model accepts interleaved text and images
while the standalone quantized text-model GGUF needs the separate mmproj artifact for image input.
Reasoning strength is controllable through low
medium, high, and xhigh settings in the system prompt.
Tradeoffs to know
- Meta's benchmark results are vendor-reported and should be reproduced with the intended scaffold, runtime, quantization, and reasoning setting.
- Agentic deployments require application-level permissions, prompt-injection defenses, validation, and human confirmation for irreversible actions.
- Audio input and output are not supported; video is processed as frames and is not an explicitly optimized modality.
- Runtime support for the new Muse Glimmer architecture, perception projector, and DFlash drafter may lag the weight release.
When not to use this
- Self-hosting outcomes depend on hardware, quantization, and ops maturity—budget time beyond swapping an API hostname.
- May demand more instrumentation than SaaS-managed APIs to duplicate latency, failover, and support guarantees.
- Benchmark prompts and regressions continuously before rewriting entire routing tables around weights.
Technical specs
- Inputs
- text, image
- Outputs
- text
- Capabilities
- agentic task completion, coding, tool use, multimodal reasoning, failure recovery, controllable reasoning effort, multilingual generation, local deployment
- License
- Apache 2.0
- Model string
muse-glimmer-30b
Benchmarks
{
"source": "https://huggingface.co/meta-models/Muse-Glimmer-30B",
"mmmu_pro": 74,
"aime_2026": 94.7,
"beam_128k": 65.1,
"deepsearch_qa": 74.6,
"swe_bench_pro": 51.2,
"vendor_reported": true,
"mcp_atlas_public": 75.5,
"swe_bench_verified": 76
}What Meta announced on August 10, 2026
Meta released the Muse Glimmer weights under Apache 2.0 as part of Mark Zuckerberg's announcement that the company is resuming open model releases. The announcement separately says Meta plans to open weights for a version of Muse Spark 1.2 in the coming weeks; that future Spark release is not the Muse Glimmer checkpoint available today.
- Muse Glimmer 30B full-precision and quantized artifacts are publicly downloadable now.
- Treat Muse Spark 1.2 open weights as announced but not yet released until Meta publishes the checkpoint and license.
- Use the exact model name in evaluations and telemetry because Glimmer and Spark target different capability and deployment lanes.
Sources: Meta Muse Glimmer model card, Axios interview and announcement summary
Muse Glimmer 30B architecture and modalities
Meta documents Muse Glimmer as a dense causal transformer with approximately 29.6 billion parameters including a dedicated roughly 1.8B-parameter ViT-G/14 perception encoder. It accepts text and images, generates text, and has a documented context length of 131,072+ tokens.
- The language model uses 52 layers and a repeating local-local-local-global attention pattern.
- The model card lists a January 4, 2026 knowledge cutoff and training across more than 100 languages.
- The perception path supports screenshots, charts, documents, and images; audio is not supported.
Muse Glimmer local deployment options
Meta publishes BF16 weights plus two GGUF quantizations. The official model card targets the full-precision build at 64 GB VRAM, the dynamic K-quant build at 32 GB, and the 17 GB K-quant build at 24 GB. These are Meta's tested target envelopes, not universal minimum requirements.
- The GGUF text model runs without vision; add mmproj-kquant.gguf for image input.
- The optional dflash-kquant.gguf drafter enables speculative decoding only when the selected runtime supports the released DFlash integration.
- Start with a smaller context than 131K and measure KV-cache memory, prompt-processing time, generation speed, and task quality on the actual device.
Sources: Official Muse Glimmer GGUF artifacts
Agent safety and evaluation checklist
A locally running model can still create external risk when connected to files, browsers, shells, credentials, or business systems. Evaluate the complete agent scaffold rather than treating the base model's benchmark table as a deployment approval.
- Validate tool arguments and restrict each tool to the minimum identity, filesystem, network, and data permissions required.
- Require confirmation for purchases, messages, account changes, production writes, secrets access, and destructive actions.
- Test prompt injection, recovery from failed tools, long-horizon drift, multilingual quality, privacy boundaries, and quantization regressions.
Sources: Muse Glimmer trust, safety, and limitations, Meta evaluation methodology
Meta Muse family lineup
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Muse Glimmer 30B FAQ
What is Muse Glimmer 30B?
Muse Glimmer 30B is Meta Superintelligence Labs' open-weight multimodal model for local agents, coding, tool use, long-horizon reasoning, and image understanding. Meta's model card documents a dense 29.6B-parameter architecture with a dedicated perception encoder, 131,072+ context, text-and-image input, text output, c...
When does Muse Glimmer 30B fit best?
Local coding and research agents
What should teams watch out for with Muse Glimmer 30B?
Meta's benchmark results are vendor-reported and should be reproduced with the intended scaffold, runtime, quantization, and reasoning setting.
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