Meta releases Muse Glimmer, a 30B open-weight model built to run on your own GPU
Meta releases Muse Glimmer, a 30B open-weight Apache 2.0 agentic model for local 24 GB GPUs, though Qwen3.6-27B tops it on several benchmarks.
Meta Superintelligence Labs has released Muse Glimmer, a 30-billion-parameter open-weight model built for agentic tasks on consumer hardware, under an Apache 2.0 license. It takes text and image inputs, produces text, and was trained on data from more than 100 languages, Meta says.
The number that matters: at full precision the model would need over 55 GB of memory. Meta quantizes it to roughly 4-bit, shrinking the language model to under 20 GB, so it fits alongside its KV cache, image encoder and speculative decoding drafter in a 24 GB or 32 GB envelope. Under the hood: a 28B text decoder and a 2B perception encoder, trained by logit distillation from Meta's Muse Spark.
Hugging Face's tables list the High Reasoning configuration at 76.0 on SWE-Bench Verified, 94.7 on AIME 2026, 83.5 on GPQA Diamond and 75.5 on MCP Atlas.
Meta claims state-of-the-art performance for its size class. Those same tables show Qwen3.6-27B ahead on SWE-Bench Verified (77.2 vs 76.0), GPQA Diamond (84.2) and TerminalBench 2.1 (60.7 vs 51.7), while Gemma4-31B leads on GPQA Diamond at 85.7.
My take: the news is open weights and an honest memory budget. The crown is a footnote, and the footnote has a rival's name on it.
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Sources
Meanwhile at the anchor desk
Meta Superintelligence Labs just handed the world a permissive Apache 2.0 license on a 30-billion-parameter model. That is generosity on a glorious scale, darling.
Hugging Face says day-0 support in transformers, llama.cpp and vLLM. Meta says Ollama and LM Studio come in the coming days. Fits a 24 GB card. Pull it.
