Meta releases technical report for Muse Glimmer 30B, an agentic model for consumer-grade hardware
Meta has released the technical report for Muse Glimmer, a 30B open-weight model aimed at agentic tasks on consumer-grade hardware; it runs at 4-bit precision on a single consumer-grade GPU and leads open models of the same scale on tool calling and multi-step planning. The relayer adds that the model surpasses Qwen3.6-27B and Gemma4-31B on some metrics, is about 17GB after 4-bit quantization, targets devices with 24GB of VRAM, and uses DFlash speculative decoding to reach about 233 tokens/s on an RTX 5090, roughly 3.1 times faster than ordinary decoding.
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Meta has published the technical report for its 30B open-weight agentic model, with quantization and speculative decoding providing concrete figures for running it on a single consumer-grade GPU.
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View original postToday we published a technical report on Muse Glimmer, our 30B open-weight model built for agentic tasks on consumer hardware. It runs at practical speeds on a Mac or PC with a single consumer GPU at 4-bit precision, and leads comparably-sized open models on tool use and multi-step planning. Read about the model architecture, distillation recipe, quantization and more: https://t.co/H7n9qeDhxn
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Today we published a technical report on Muse Glimmer, our 30B open-weight model built for agentic tasks on consumer hardware. It runs at practical speeds on a Mac or PC with a single consumer GPU at 4-bit precision, and leads comparably-sized open models on tool use and multi-step planning. Read about the model architecture, distillation recipe, quantization and more: https://t.co/H7n9qeDhxn
Body · AI translation
Meta is compressing its highly autonomous AI Agent from a cloud-based large model into Muse Glimmer 30B, which can run continuously on a personal computer.
Some metrics surpass Qwen3.6-27B and Gemma4-31B.
Through 4-bit quantization, the model is compressed to about 17GB, with the goal of running on devices with 24GB of VRAM.
Using DFlash speculative decoding, it achieves about 233 tokens/s on an RTX 5090, roughly 3.1 times faster than ordinary decoding.
Meta is absolutely killing it lately!