You May Be Able to Run Meta’s Latest AI Model Locally on Your Computer.
On Monday , Meta announced its latest AI model : Muse Glimmer. However, the company isn’t positioning it as the biggest and best. While Meta talks a lot about Muse Glimmer’s performance potential, the company sees two key advantages: its open architecture and the ability to run locally on consumer Macs and PCs—though perhaps not on your computer.
What is Muse Glimmer?
According to Meta, Muse Glimmer is a 30 billion-parameter AI model optimized for “always-on, local agent workflows.” This means Glimmer is designed to run autonomously directly on your computer, rather than offloading processing to the cloud. Local AI is much more secure than cloud-based AI, as your data remains within your system. Using cloud servers puts you entirely at the mercy of the company that owns the network, potentially putting your data at risk.
While models like Muse Glimmer have a variety of applications, Meta emphasizes their use in agent-based AI. AI agents can perform tasks on your behalf and are becoming increasingly popular, as users and developers can task their agents with a wide variety of functions. Running agents locally is also quite popular, as evidenced by the infamous OpenClaw bug , but the most demanding models also require powerful hardware. If you’re working with a laptop, you may not have enough power to run AI agents.
This is part of Meta’s presentation. The company claims that Muse Glimmer was trained to balance performance with the limited capabilities of its hardware. To achieve this, Meta states that Muse Glimmer goes through three training stages: “Pre-training,” in which Muse Glimmer was trained on Muse Spark output; “Intermediate training,” in which the model was trained on more intensive data; and “Post-training,” in which the model was refined across a range of parameters. Meta claims that Muse Glimmer can handle end-to-end agent task execution, multi-step reasoning, tool invocation failure recovery, and cross-model input and reasoning, among other features. It is also trained on data from over 100 languages. The company claims that the model demonstrates “high-class performance” on many standard LLM benchmarks.
While Muse Glimmer isn’t as resource-hungry as other models, it likely won’t run well on the average laptop. Meta claims that a 30 billion parameter model typically requires over 55GB of memory, but thanks to “quantization techniques,” Muse Glimmer can use less than 20GB. Factoring in “working memory,” Muse Glimmer will likely use 24 or 32GB of memory. If you have a MacBook or a “Pro” PC, this may be sufficient. But if your laptop, like mine, has 16GB or less of RAM, that’s not quite what Muse Glimmer requires. Still, it’s a step up for local AI use. (For reference, Meta claims to have tested Muse Glimmer on MacBook M4 Max, M5 Max, and with an RTX-5090 GPU.)
Meta claims that Muse Glimmer is an “open” model, meaning that Meta has published the model’s training weights. This means anyone can not only use the model but also customize it. If you have specific needs for your AI model, you can experiment with Muse Glimmer to better suit your needs.
How does Muse Glimmer compare to other AI models?
There are numerous AI models on the market today, but Meta offers direct comparisons with only two other models: Google’s Gemma4-31b and Alibaba’s Qwen3.6-27B. According to Meta’s benchmarks, Muse Glimmer outperforms 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 leads in four tests, while Qwen3.6-27B leads in eight.
Meta didn’t provide similar comparisons with industry leaders like OpenAI, DeepSeek, Z.ai, or Moonshot AI. However, Artificial Analysis did publish a ranking of all open-weight models on the market . At the time of writing, Muse Glimmer (high) ranks 18th. Moonshot AI’s Kimi K3 (maximum) leads the pack, Z.ai’s GLM-5.2 (maximum) is second, DeepSeek’s V4 Flash (maximum) is third, and Kimi K3 (low) is fourth. For comparison, Gemma4-31b is 32nd, and Qwen3.6-27B is 17th. There are many variables at play, but this leaderboard gives us a quick overview of how Meta’s latest model ranks among its competitors.
How to try Muse Glimmer
You can try Muse Glimmer right now by downloading weights from Hugging Face . Meta reports that the model will become available in the coming days in apps like Ollama, LM Studio, and Unsloth.