Meta Muse Glimmer – open weights 30B local coding model
Introducing Muse Glimmer: An Open Agentic Model That Runs on Your DeviceAugust 10, 2026·6 minute readToday, we're introducing Muse Glimmer, the next model from Meta Superintelligence Labs, and open sourcing the model weights under a permissive Apache 2.0 license. Muse Glimmer is a 30-billion-parameter model optimized for always-on local agent workflows. It’s small enough to run on a Mac or PC with a single consumer GPU, enabling use cases that range from local agents and function calling, to local coding, and LLM-as-a-judge evaluation.
- ▪Introducing Muse Glimmer: An Open Agentic Model That Runs on Your DeviceAugust 10, 2026·6 minute readToday, we're introducing Muse Glimmer, the next model from Meta Superintelligence Labs, and open sourcing the model weights under a permiss
- ▪Muse Glimmer is a 30-billion-parameter model optimized for always-on local agent workflows.
- ▪It’s small enough to run on a Mac or PC with a single consumer GPU, enabling use cases that range from local agents and function calling, to local coding, and LLM-as-a-judge evaluation.
4 outlets in our directory ran this story, first to last over 2 hours. All of the coverage we found sits in one bucket: centre. That one-sidedness is itself worth noticing.
- ▪ Meta's 'open source' Muse Glimmer model can run on a single computer — Engadget
- ▪ Meta Publishes Muse Glimmer As 30B Open Agentic Model — Phoronix
- ▪ Meta's new open-weight model targets local agentic AI — X (formerly Twitter)
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Meta AI Research |
| Canonical URL | https://research.meta.ai/blog/introducing-muse-glimmer-open-agentic-model |
| Publication time | Mon, 10 Aug 2026 10:10:02 +0000 |
| Retrieval time | 2026-08-10T10:55:42.055Z |
| Last seen | 2026-08-10T10:55:42.055Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | Ow17dmTaQ1UA · 4 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Introducing Muse Glimmer: An Open Agentic Model That Runs on Your DeviceAugust 10, 2026·6 minute readToday, we're introducing Muse Glimmer, the next model from Meta Superintelligence Labs, and open sourcing the model weights under a permissive Apache 2.0 license. Muse Glimmer is a 30-billion-parameter model optimized for always-on local agent workflows. It’s small enough to run on a Mac or PC with a single consumer GPU, enabling use cases that range from local agents and function calling, to local coding, and LLM-as-a-judge evaluation. Muse Glimmer delivers strong performance on key agentic use cases and benchmarks compared with leading models in its size category.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Meta AI Research.
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