Muse Glimmer 30B

Local AI Zone's comprehensive technical analysis of Muse Glimmer 30B (released Aug 10, 2026 by Meta Superintelligence Labs, Apache 2.0, Hugging Face model card) presents the model as a 29.6B-parameter dense multimodal language model purpose-built for autonomous agentic tasks on consumer hardware. It reports 27.8B langu The analysis highlights Muse Glimmer 30B's benchmark headline of 75.5% on MCP-Atlas, 74.6% on DeepSearch QA, and 51.2% on SWE-bench Pro while running entirely on-device with no cloud dependencies, positioning it as a frontier-class local agent model with 24GB VRAM minimum. A notable technical contribution is Meta's inc

Kingy.ai's launch-day analysis of Muse Glimmer 30B (released Aug 10, 2026 by Meta Superintelligence Labs) frames the model as a serious option for local agents on 24GB or 32GB machines, citing Meta-supplied calibrated GGUFs, vision and speculative-decoding components, a 131K context configuration, and broad agent evalu The article adds useful caveats for buyers: Qwen3.6-27B wins several practical agent and multimodal tests in Meta's own benchmark table, and independent like-for-like reproduction is still needed. Other sourced details include Apache 2.0 weights paired with Meta Usage Policy, more than 100 claimed training languages, a

Evals.report's consolidated benchmark index for Muse Glimmer 30B tracks 14 reported scores across categories including reasoning, tool use, coding, agents, and multimodal, each shown with its source status and date. Verified rows on the page include SWE-bench Verified at 76.0% resolved, SWE-bench Pro at 51.2% resolved, An official (non-reproduced) row lists AA-Omniscience: Knowledge and Hallucination Benchmark at a -33 AA-Omniscience Index score, a notable negative headline that stands out from the otherwise positive benchmark profile. The page explicitly notes that scores are never combined into a single ranking and that "Verified"

Wavect's local agent guide for Muse Glimmer 30B positions the model as Meta's distilled Muse Spark variant for always-on agents, local coding, function calling, and LLM-as-a-judge work, targeting 24GB and 32GB consumer hardware. The model card is reported to link quantizations for llama.cpp, Ollama, and LM Studio, docu The buyer-focused angle distinguishes this piece: it is framed as a procurement decision for product teams piloting Muse Glimmer for a private local agent, walking through hardware fit, agent benchmark interpretation, commercial licensing, and a production pilot framework that yields a buying recommendation. Last revie