Meta returns to open source with Muse Glimmer
A 30-billion-parameter model under Apache 2.0 that runs on a single consumer GPU – built for local AI agents.
Illustration · AI-generated (AI IN LIFE)
Meta has released Muse Glimmer, a new open-weights model with 30 billion parameters under the permissive Apache 2.0 license.
At a glance
- 30B parameters, open weights under Apache 2.0
- Context window: 131,072 tokens, text and image input, 100+ languages
- Runs 4-bit quantized in ~24 GB VRAM on a consumer GPU
- MCP Atlas benchmark: 75.5 – best in its size class, per Meta
- Up to 3.1x speedup via DFlash on an RTX 5090
Meta has released Muse Glimmer, a new open-weights model with 30 billion parameters under the permissive Apache 2.0 license. After stepping back from open Llama releases, observers read the move as Meta’s return to an open-source strategy – this time with a clear focus: local, agentic applications.
Technically, the model offers a 131,072-token context window, handles text and image input, and supports more than 100 languages. In its 4-bit quantized variant it runs in roughly 24 gigabytes of VRAM on a single consumer GPU – with only about one percent accuracy loss, according to Meta. With speculative decoding (DFlash), it reaches up to a 3.1x speedup on an RTX 5090.
On agent benchmarks, Meta says the model leads its size class: it scores 75.5 on the MCP Atlas benchmark, well ahead of comparable models. It trails, however, on classic computer-vision tasks.
Strategically, Meta is targeting industries that can’t or won’t send AI workloads to the cloud: healthcare, law firms, financial services and other regulated sectors that need data residency and offline operation. Always-on agents on your own hardware – that is the scenario Muse Glimmer was built for.
FAQ
What is Muse Glimmer?
An open 30-billion-parameter Meta model under Apache 2.0, optimized for local agentic applications and tool use.
What hardware do I need?
In 4-bit quantization, a single GPU with around 24 GB of VRAM is enough – a high-end consumer card.
Who is the model for?
Deployments with data-residency or offline requirements, such as healthcare, law firms and financial services.
