Meta Superintelligence Labs released Muse Glimmer, a 30B-parameter model built for always-on local agents on one consumer GPU.

weights on HuggingFace under an Apache 2.0 license,

• At full precision the model would need 55GB+ of memory.

• Meta compressed the weights to roughly 4-bit precision, shrinking the language model to under 20GB. That leaves room inside a 24GB or 32GB envelope for the KV cache, a perception encoder for screenshots, and a speculative decoding drafter.

• The compressed model still leads Gemma4-31B and Qwen3.6-27B on MCP Atlas, at 75.5 against 54.2 and 62.5.

That lead reverses on computer use and terminal coding, where Qwen3.6-27B scores higher on OSWorld-Verified, TerminalBench 2.1, and SWE-Bench Verified.

• Instead of writing one word at a time, Muse Glimmer lets a small helper model called DFlash guess a whole block ahead. The main model then checks that block in one pass, keeping the correct guesses and fixing the wrong ones.

• On an RTX-5090, that lifted decode speed from 74.9 to 233 tokens per second, a 3.1x gain measured through llama.cpp.

• Apple hardware gains less, 1.8x on an M5-Max and 1.5x on an M4-Max, reaching 50 and 38 tokens per second.