Meta Launches Muse Glimmer AI Model for Local Coding, Agentic Tasks and Multi-Step Reasoning

Meta has made the Muse Glimmer open weights available on Hugging Face

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Written by Nithya P Nair, Edited by Rohan Pal | Updated: 11 August 2026 11:11 IST
Highlights
  • Muse Glimmer is designed for a wide range of agentic AI tasks
  • Muse Glimmer supports multimodal reasoning
  • Muse Glimmer can operate on a Mac or PC with a single consumer GPU

Meta says Muse Glimmer has been trained on data covering more than 100 languages

Photo Credit: Meta

Meta has introduced Muse Glimmer, a new AI model developed by its Meta Superintelligence Labs. The Muse Glimmer features 30 billion parameters and is optimised for always-on local agent workflows. Meta says it is suitable to run on a Mac or PC equipped with a single consumer GPU. At full precision, Muse Glimmer would require more than 55GB of memory. The Muse Glimmer is designed to handle functions including agentic task completion, multi-step reasoning, and multi-model input and reasoning.

Meta Launches Muse Glimmer

In a newsroom post on Monday, Meta has announced Muse Glimmer. This latest AI model from Meta Superintelligence Labs has the model weights under a permissive Apache 2.0 license. "Muse Glimmer is a 30-billion-parameter model optimised for always-on local agent workflows", said Meta. The company says the model can work on a Mac or PC with a single consumer GPU. This would enable use cases including local agents and function calling, local coding, and LLM-as-a-judge evaluation.

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Meta claims that the Muse Glimmer offers strong performance on key agentic use cases and benchmarks compared with leading models in the same size category. The company says many AI applications still depend on cloud infrastructure and network access, and Muse Glimmer is designed to address this limitation and is optimised for local use cases. It is claimed to allow Running models locally with or without an internet connection.

The 30-billion-parameter model would require more than 55GB of memory at full precision. Meta uses 4-bit quantisation to reduce the model size to under 20GB, and this lets it run on a 24GB or 32GB memory setup. Meta has also shared a direct comparison of the Muse Glimmer with Google's Gemma4-31b and Alibaba's Qwen3.6-27B. The results show that Muse Glimmer dominates both models in 12 tests.

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According to Meta, Muse Glimmer is built for end-to-end agentic task completion, reliable tool use, multimodal reasoning, error diagnosis and tool retries, multi-step reasoning, among others. It is also trained on data from over 100 languages.

Users can download the weights from Hugging Face. Meta says the Muse Glimmer will be available in the coming days from apps like Ollama, LM Studio, and Unsloth. The tech giant confirmed that it is working with AMD, Arm, Dell, Intel, and Nvidia to optimise performance across devices. Meta confirmed that the optimised integrations for llama.cpp, MLX and ExecuTorch will be available in the coming days.

 

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