小扎高调开战闭源AI！推30B智能体模型，消费级显卡就能跑，杨立昆点赞
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No observed public metrics; popularity remains neutral/archived.
Merged summary
TL;DR - Meta released Muse Glimmer, an open-weight 30B local agent model designed for multimodal tool use and multi-step tasks on consumer hardware. It advances private, offline personal agents, though latency, token usage, and tooling remain limitations.
- Apache 2.0 weights are available; 4-bit quantization shrinks the model from over 55GB to under 20GB for systems with 24–32GB memory.
- Muse Glimmer supports text and images, structured tool calls, error recovery, long-running workflows, and over 100 languages.
- Meta reports 12 leading results across 22 non-safety benchmarks, particularly in agent tasks, but competitors lead some desktop, coding, multimodal, and safety tests.
- DFlash speculative decoding reportedly improves generation speed by up to 3.1×, while independent tests demonstrated offline Mac control and local coding.
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小扎高调开战闭源AI！推30B智能体模型，消费级显卡就能跑，杨立昆点赞
Public signals
N/A
TL;DR - Meta released Muse Glimmer, an open-weight 30B local agent model designed for multimodal tool use and multi-step tasks on consumer hardware. It advances private, offline personal agents, though latency, token usage, and tooling remain limitations.
- Apache 2.0 weights are available; 4-bit quantization shrinks the model from over 55GB to under 20GB for systems with 24–32GB memory.
- Muse Glimmer supports text and images, structured tool calls, error recovery, long-running workflows, and over 100 languages.
- Meta reports 12 leading results across 22 non-safety benchmarks, particularly in agent tasks, but competitors lead some desktop, coding, multimodal, and safety tests.
- DFlash speculative decoding reportedly improves generation speed by up to 3.1×, while independent tests demonstrated offline Mac control and local coding.