Muse Glimmer

A 30-billion-parameter open-weights agentic model under Apache 2.0 that runs locally on a single consumer GPU.

Handles local agents and function calling, local coding, and LLM-as-a-judge evaluation, with end-to-end task completion, multi-step reasoning, failure recovery, and multimodal input
Trained in three phases: pre-training by logit distillation from a larger teacher model, mid-training on longer-context agentic data, then supervised fine-tuning combined with reinforcement learning
30 billion parameters, with quantized versions compressed under 20 GB
Released under Apache 2.0, a permissive license that allows commercial use and modification
Runs on a Mac or PC with a single consumer GPU, validated on MacBook M4-Max, M5-Max, and RTX-5090, needing 24-32 GB of memory with quantization and speculative decoding
Available on Hugging Face at huggingface.co/meta-models/Muse-Glimmer-30B, with llama.cpp, MLX, and ExecuTorch integrations named as coming