Introducing Muse Glimmer, an open-weight 30B-parameter model optimized for local, always-on agent workflows. Muse…

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Summary

Muse Glimmer is an open-weight 30B-parameter AI model optimized for local agent workflows that runs on consumer hardware like Macs and PCs. It achieves low latency through quantization and a lightweight drafter model, enabling real-time multi-step task automation entirely on your device, with weights released under Apache 2.0 license.

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#1
Introducing Muse Glimmer, an open-weight 30B-parameter model optimized for local, always-on agent workflows. Muse Glimmer delivers strong performance on key agentic use cases and benchmarks compared with leading models in its size category, and is designed to run entirely on consumer hardware like a Mac or PCs with performant GPUs. In keeping with our long tradition of sharing fundamental AI research, we’re releasing model weights under a permissive Apache 2.0 license. 🧵👇
#2
For a local agent to be practical, generation latency must be low enough to maintain workflow continuity. To run Muse Glimmer on consumer hardware without degrading quality, we used quantization to shrink the language model to under 20GB and a lightweight DFlash drafter model to accelerate token generation. As a result, Muse Glimmer is fast enough for fluid conversation and real-time agent interaction, all running entirely on your device.
#3
Muse Glimmer can complete multi-step agentic tasks end-to-end from a single natural language prompt. In this demo, it autonomously discovers a local Home Assistant instance via network tool calls, queries device APIs, writes a responsive HTML/CSS/JS dashboard from scratch, and deploys a local server for verification.
#4
🔗 Download Muse Glimmer on @huggingface: huggingface.co/meta-models 🔗 Read the technical blog: go.meta.me/museglimmer 🔗 Find resources: developer.meta.com/ai/models/muse…