Meta’s Muse Glimmer is a 30B dense transformer with a visual encoder that, after 4‑bit quantization, runs within 24‑32 GB VRAM, achieves agentic benchmark strengths, and uses DFlash speculative decoding to reach 233 tok/s, enabling always‑on, high‑frequency local AI agents on consumer hardware.
Meta has open‑sourced its 29.6‑billion‑parameter Muse Glimmer model under Apache 2.0, offering a 4‑bit quantized version that fits on a single high‑end GPU, while benchmark results show strong agent performance but notable hallucination and accuracy gaps compared with competing models.
Meta’s Muse Glimmer is a 30‑billion‑parameter, open‑source dense model engineered to run continuously on consumer‑grade hardware with as little as 24 GB VRAM, using 4‑bit quantization and DFlash speculative decoding to retain multimodal, long‑context and agentic capabilities while achieving up to 233 tok/s throughput.
Meta’s newly open‑sourced 30‑billion‑parameter Muse Glimmer agent model runs on a single consumer‑grade GPU, outperforms Gemma‑4 and Qwen‑3.6 on multiple Agent benchmarks, uses a perception encoder for multimodal input, and fits into a 20 GB memory envelope through quantization and a lightweight drafter.
Meta has unveiled Muse Glimmer, a 30‑billion‑parameter open‑source LLM under Apache 2.0, positioned for agent workloads and benchmarked against Google’s Gemma 4 and Alibaba’s Qwen, while highlighting hardware requirements, performance limits, and the broader strategic implications for U.S. AI policy.
