Meta has released Muse Glimmer, a 29.6-billion-parameter dense causal transformer released under the Apache 2.0 license, designed specifically for local agentic coding and personal assistant workloads. Unlike recent sparse Mixture-of-Experts models, Glimmer utilizes a dense architecture to optimize for single-GPU inference on consumer hardware. Technical specifications include a 131,072+ token context length supported by a sliding window attention pattern (three local layers for every one global layer) and a 1.8B parameter ViT-G/14 vision encoder. To accelerate generation, Meta integrated DFlash, a block-diffusion drafter that predicts 16-token blocks for speculative decoding. For deployment, Meta provided 4-bit quantized GGUF variants, including a 16.8GB K-Quant-17GB version for 24GB VRAM cards, and ensured day-one support for llama.cpp (build b10353). While Glimmer leads in agentic benchmarks like MCP Atlas (75.5) and DeepSearch QA (74.6), it trails Alibaba's Qwen3.6-27B in several key areas, such as GDPval-AA and OSWorld-Verified. Notably, Meta's own safety benchmarks show Glimmer trails Google's Gemma4-31B in violation rates, recording a 26.4 violation rate on CI Memories compared to Gemma4's 12.1, despite Glimmer's intended use for sensitive personal data access.