Meta has released Muse Glimmer, a 30B-parameter open-weight LLM specifically architected for autonomous agentic workloads on consumer hardware. To optimize for long-horizon tasks, the model utilizes a unique [Local×3, Global] attention pattern and an aggressive 16:1 grouped-query attention (GQA) ratio, which significantly reduces KV cache memory requirements. Glimmer features a 1.8B ViT-G/14 vision encoder, enabling multimodal capabilities such as direct screen-state reasoning without OCR. The model was trained via a three-phase distillation process from the frontier-scale Muse Spark, utilizing logit distillation, long-context agentic trajectories, and reinforcement learning to ensure operational discipline. For deployment, Meta provides K-Quant mixed-precision quantization, allowing the 17GB variant to run on 24GB VRAM with only 1.0% average degradation. Performance is significantly boosted by DFlash speculative decoding, which uses a block-diffusion drafter to achieve 233.4 tokens per second on an NVIDIA RTX 5090—a 3.1× speedup over standard autoregressive decoding. Benchmarks highlight its efficacy, with Glimmer scoring 75.5 on the Scale AI MCP-Atlas leaderboard and 94.7% on AIME 2026, demonstrating superior reasoning and tool-calling capabilities compared to several contemporary models.