Meta open-sources Muse Glimmer, a 30B agentic model that runs locally on a single GPU

Meta Muse Glimmer – open weights 30B local coding model

Meta released Muse Glimmer weights under Apache 2.0. It's a 30B model built for always-on local agent workflows, small enough to run on a Mac or PC with a single consumer GPU. 4-bit quantization shrinks it below 20 GB, leaving room for KV cache and the vision encoder within a 24 GB or 32 GB envelope. Training used logit distillation from a larger Muse Spark teacher, followed by mid-training on long-context agent data and post-training with SFT, on-policy distillation, and RL. Meta's benchmarks show it outperforming Gemma4-31B and Qwen3.6-27B on agentic, coding, multimodal, and safety evals. The post doesn't disclose specific latency numbers, only that inference optimizations were applied to keep it responsive.

Why it matters: Meta drops a 30B local agent model under Apache 2.0, quantized under 20 GB for consumer GPUs. Clear positioning — not a general chatbot but purpose-built for always-on agent workflows. Score held back from higher bands because we only have the launch blog; third-party benchmar...