Meta has introduced Muse Glimmer, a 30-billion-parameter AI model designed to run AI agents locally on consumer hardware. Mark Zuckerberg’s company is releasing the model weights under the Apache 2.0 licence allowing developers to download, modify and deploy the model for their own applications. It is aimed at tasks such as coding, function calling, tool use and multi-step agent workflows. Meta says it can run on a Mac or PC with a single consumer GPU, reducing the need for cloud-based infrastructure or a constant internet connection.
Meta says Muse Glimmer has been trained to handle longer workflows, including tool calls, reasoning, coding and failure recovery. The model can identify failed tool calls and attempt to correct them instead of stopping the task. It also supports the multimodal inputs allowing agents to work with text and images such as screenshots, charts and documents. The model has been trained using data from more than 100 languages.
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Meta used outputs from its larger Muse Spark model during pre-training through logit distillation. The company then added longer-context and agent-focused training, followed by supervised fine-tuning, reinforcement learning and further distillation across reasoning, coding and agentic tasks.
To run a 30B model at full precision would require more than 55GB of memory, according to Meta. The company has therefore used quantisation to reduce the model to under 20GB at roughly 4-bit precision. This allows it to operate within systems offering 24GB or 32GB of memory while leaving room for its working memory and image-processing components.
Meta is also using speculative decoding to improve response speeds. A smaller companion model proposes groups of tokens, which Muse Glimmer then verifies. Meta says this increased decoding speed by up to 3.1 times on an Nvidia RTX 5090, 1.8 times on an M5 Max and 1.5 times on an M4 Max.
Muse Glimmer is available through Hugging Face. Support for platforms and frameworks including Ollama, LM Studio, llama.cpp, MLX and ExecuTorch is expected to follow.