On Monday, Meta announced its latest AI model: Muse Glimmer. The company isn't necessarily advertising this model as its biggest and best, however. While Meta has plenty to say about Muse Glimmer's performance potential, there are two key selling points for the model in the company's eyes: The fact that it's open-weight, and that it's designed to run locally on "consumer" Macs and PCs—though perhaps not on your computer.

What is Muse Glimmer?

According ot Meta, Muse Glimmer is a 30-billion-parameter AI model optimized for "always-on local agent workflows." What that means is Glimmer is designed to run autonomously directly on your machine, rather than outsourcing that processing to the cloud. Local AI is much more secure than cloud-based, as your data stays within your system. When you tap into cloud servers, you're at the mercy of whichever company owns that network, potentially putting your data in jeopardy.

While there are many use cases for a model like Muse Glimmer, Meta is emphasizing its use for agentic AI. AI agents can perform tasks on your behalf and are growing in popularity, as users and developers can ask their agents to do any number of functions. Running agents locally is also quite popular, as evidenced by OpenClaw's infamous year, but the most demanding models also require powerful hardware. If you're working with a laptop, you might not have the energy necessary to run AI agents.

That's part of Meta's pitch here. The company says Muse Glimmer was trained to balance performance with limited hardware. Meta says it puts Muse Glimmer through three sets of training to achieve this: a "Pre-Training," which trained Muse Glimmer on Muse Spark's outputs; "Mid-Training," which trained the model on more "agent-heavy" data; and "Post-Training," which fine-tuned the model across a number of parameters. Meta says Muse Glimmer can handle end-to-end agentic task completion, multi-step reasoning, recovery when a tool call fails, and multi-model input and reasoning, among other functions. It is also trained on data from over 100 languages. The company says the model performs "strongly for its size class" against many standard LLM benchmarks.

While Muse Glimmer isn't as demanding as other models, it likely won't run well on the average laptop. Meta says that a 30-billion parameter model would typically require more than 55GB of memory, but through "quantization techniques," Muse Glimmer can use less than 20GB. When you take its "working memory" into consideration, Muse Glimmer will likely use 24GB or 32GB of memory. If you have a "Pro" MacBook or PC, that might suit you well. But if you're like me, your laptop has 16GB or less of RAM—not quite what Muse Glimmer requires. Still, it's a step forward for local AI use. (For reference, Meta says it tested Muse Glimmer on MacBook M4 Max, M5 Max, and with an RTX-5090 GPU.)

Meta says Muse Glimmer is "open," which means Meta released the model's training weights. As such, not only can anyone use the model, but they can tune it as well. If you have specific needs for your AI model, you can tinker with Muse Glimmer to better serve your usage.

How Muse Glimmer compares to other AI models

There are a lot of AI models on the market these days, but Meta only has direct comparisons with two other models: Google's Gemma4-31b and Alibaba's Qwen3.6-27B. According to Meta's benchmarks, Muse Glimmer beats both models in the following 12 tests: MCP Atlas, DeepSearch QA, 𝜏³-banking, WildClawBench, GAIA2, SWE-Bench Pro, Sci Code, Charxiv Reasoning, IFBench, AIME 2026, AA-LCR, and Beam 128K. Gemma4-31b still has the crown in four of the benchmarks, while Qwen3.6-27B leads in eight.

Meta didn't provide similar comparisons with models from industry leaders, like OpenAI, DeepSeek, Z.ai, or Moonshot AI. However, Artificial Analysis has a ranking of all open-weight models on the market. As of this article, Muse Glimmer (high) is actually in 18th place. Moonshot AI's Kimi K3 (max) is in the lead, with Z.ai's GLM-5.2 (max) in second, DeepSeek's V4 Flash (max) in third, and Kimi K3 in (low) in fourth. For reference, Gemma4-31b is in 32nd, while Qwen3.6-27B is actually in 17th. There are many variables here, but this leaderboard gives us a quick glance at how Meta's latest model ranks amongst the competition.

How to try Muse Glimmer

You can try Muse Glimmer right now by downloading the weights from Hugging Face. Meta says the model will be available in the coming days from apps like Ollama, LM Studio, and Unsloth.