Meta released Muse Glimmer, a 30-billion-parameter AI model unveiled on August 10 that runs on a single consumer GPU and fits in under 20 GB, requiring no data center or internet connection. It is a distilled version of Meta’s larger Muse Spark 1.2, compressed via logit distillation, fine-tuning, quantization, and speculative decoding. The model targets agentic tasks like schedule management, file organization, tool use, coding assistance, and multimodal reasoning, supporting over 100 languages. Meta published the weights on Hugging Face under the Apache 2.0 license, integrating with llama.cpp and MLX, and released developer tools and responsible-use guidance. This follows Meta’s pattern of open-weight releases, including the original Muse Spark model in April 2026 and iterative updates in July. Local execution eliminates data leaving the machine, reduces latency from cloud round-trips, and converts variable cloud inference costs into fixed hardware costs. The tradeoff is capability, as a 30-billion-parameter model running locally won’t match larger models from OpenAI, Google, or Meta’s own Muse Spark 1.2, which require specialized hardware and constant cloud connectivity.