Meta is back with Muse Glimmer: local, agentic, multimodal, and open source
- ID
- 12633
- Status
- summarized
- Published
- 10 Aug 2026, 8:00 AM
- Fetched
- 10 Aug 2026, 8:15 PM
- Provider
- Hugging Face Blog
- Category
- developer-ai
- Original URL
- https://huggingface.co/blog/muse-glimmer
- Source URL
- https://huggingface.co/blog/feed.xml
Summary
- Score
- 7.5
- Created
- 10 Aug 2026, 8:16 PM
- Tags
- Audience
- developersvibe_codersai_ml_learnersai_agent_users
What happened
Meta released Muse Glimmer, a 30B parameter multimodal model distilled from Muse and licensed under Apache 2.0, optimized for local agentic use cases like coding, document analysis, and personal assistants. It ships with day-0 support in transformers, llama.cpp, vLLM, and Hugging Face Inference Endpoints. Benchmarks show it leading Gemma4-31B and Qwen3.6-27B on agentic tasks like SWE-Bench Pro (51.2) and WildClawBench (47.6), though Qwen3.6 beats it on OSWorld-Verified (75.6 vs 65.9) and TerminalBench (60.7 vs 43.4).
Why it matters
If you're building agentic workflows that need to run locally for privacy or cost reasons, Muse Glimmer gives you a 30B Apache 2.0 multimodal model with strong coding-agent benchmarks that fits on a single high-VRAM GPU via llama.cpp or vLLM. Compare its SWE-Bench Verified (76.0) and AgentDojo attack success rate (28.4) against your current local model before committing—Qwen3.6-27B may be better for terminal-heavy automation tasks.
Discussion angle
Where does a 30B local multimodal agent actually win vs API-based models in Malaysia—considering GPU availability, cost per query, and data residency requirements for government or regulated industry work?