Muse Glimmer, the Next Model From Meta Superintelligence Labs, and Open Sourcing the Model Weights Under a Permissive Apache 2.0 License

Muse Glimmer is a 30-billion-parameter model optimized for always-on local agent workflows. It’s small enough to run on a Mac or PC with a single consumer GPU, enabling use cases that range from local agents and function calling, to local coding, and LLM-as-a-judge evaluation. Muse Glimmer delivers strong performance on key agentic use cases and benchmarks compared with leading models in its size category.

Foundation models have achieved remarkable capabilities across reasoning, code generation, and tool use — yet most deployments still depend on cloud infrastructure and network access. Running models locally enables you to use AI anywhere, anytime, with or without an internet connection. This is increasingly viable: the open source community has shown that smaller models, when trained effectively, can approach frontier-level performance on targeted tasks.

Muse Glimmer is optimized for these local use cases. Keeping with our long tradition of sharing fundamental AI research, we're releasing Muse Glimmer open weights today on Hugging Face, along with developer documentation to help you start building and running your own agents. Muse Glimmer is built to work with the tools developers already use.

Optimized integrations on llama.cpp, MLX, and ExecuTorch will land in the coming days, so you can go from download to working agent in minutes. An agent that manages your schedule, drafts your messages, organizes your files, and learns how you work needs deep access to personal context. It also needs several capabilities working in concert: long-horizon execution, precise tool calling, multimodal understanding, long-context memory, and instruction following.

We designed Muse Glimmer to balance capability against the memory and compute constraints of local hardware. This required a compact architecture, a novel distillation recipe that transfers agentic reasoning from a much larger teacher model, and inference optimizations — including quantization — to meet latency expectations. We achieved this in the following phases: Muse Glimmer was evaluated under the standards set out in Meta's Advanced AI Scaling Framework and assessed for open-weight release across all relevant categories.

Building effective agents requires key capabilities working together to achieve the user’s goals. Muse Glimmer is trained and evaluated across each of the following: We evaluated Muse Glimmer across a broad range of benchmarks to assess the diverse capabilities required for effective autonomous agent behavior. Compared with Gemma4-31B and Qwen3.6-27B, Muse Glimmer performs strongly for its size class on several widely used LLM benchmarks.

For more detail about our evaluations, see our report. A local agent is truly useful if it's fast enough to feel responsive. An agent that takes minutes to reply or plan its next step breaks the flow of real work.

We applied two optimizations to make Muse Glimmer run at practical speeds on consumer hardware without sacrificing quality. At full precision, a 30-billion parameter model would require over 55 GB of memory — far more than any consumer GPU offers. We use quantization techniques to compress the model's weights to approximately 4-bit precision, shrinking the language model to under 20 GB.

This leaves enough headroom for the model's working memory (its "KV cache"), the perception encoder for image understanding, and the speculative decoding drafter to run simultaneously within a 24 GB or 32 GB envelope. We validated that this compression introduces minimal to no degradation on agentic tasks. Language models normally generate text one token at a time, which can feel slow during long reasoning chains or multi-step tool calls.