Meta Superintelligence Labs has released Muse Glimmer, a 30-billion-parameter open-weights model released under the Apache 2.0 license, specifically optimized for local, always-on agentic workflows. To balance high-level reasoning with the memory constraints of consumer hardware, the model utilizes a compact architecture and a multi-phase training regimen involving logit distillation from the larger Muse Spark model, mid-training on long-context reasoning traces, and post-training via supervised fine-tuning and reinforcement learning. Muse Glimmer excels in end-to-end task completion, tool calling, and multimodal reasoning through a dedicated perception encoder. To ensure local viability on devices like the MacBook M4-Max or RTX-5090, the model employs 4-bit quantization, reducing the memory footprint from over 55 GB to under 20 GB. Additionally, the integration of a lightweight "DFlash" drafter model enables speculative decoding, allowing the model to propose token blocks for parallel verification to significantly increase generation speeds. This combination of optimizations enables complex multi-step reasoning, failure recovery, and multilingual support (over 100 languages) to run within a 24 GB to 32 GB memory envelope, making it suitable for real-time local agents, coding, and LLM-as-a-judge applications.