Meta Superintelligence Labs has launched its latest agentic model – Muse Glimmer – and is open sourcing the model weights under a permissive Apache 2.0 licence.
Muse Glimmer is a 30-billion-parameter model optimised 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, the company says, 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 its tradition of sharing fundamental AI research, Meta says it is 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.
Optimised integrations on llama.cpp, MLX, and ExecuTorch will land in the coming days, Meta says, so you can go from download to working agent in minutes.
How Meta trained Muse Glimmer
An agent that manages your schedule, drafts your messages, organises 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, Meta says. This required a compact architecture, a novel distillation recipe that transfers agentic reasoning from a much larger teacher model, and inference optimisations – including quantisation – to meet latency expectations. We achieved this in the following phases:
- Pre-Training. We trained Muse Glimmer on Muse Spark’s outputs using logit distillation, leveraging a similar data mix as the teacher.
- Mid-Training. We trained the model on longer-context, more agent-heavy data with richer reasoning traces, alongside organic data.
- Post-Training. We combined supervised fine-tuning with a mix of on-policy distillation and reinforcement learning across general, reasoning, coding, and agentic domains.
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.
What Muse Glimmer can do
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:
- End-to-end Agentic Task Completion. Muse Glimmer achieves strong success rates on full-task benchmarks including DeepSearch QA, MCP-Atlas, 𝛕-Bench and SWE-Bench, which measure its ability to work within scaffolds, write and debug code, and resolve multi-turn requests from start to finish.
- Reliable Tool Use. The model handles a wide range of function calls, invoking tools with precise schemas throughout extended workflows.
- Multi-Step Reasoning. Muse Glimmer chains reasoning over long horizons, sustaining coherent plans across complex, extended workflows.
- Failure Recovery. When a tool call fails or returns an unexpected result, the model is trained to diagnose the error and retry rather than halt.
- Multimodal Input and Reasoning. Through a dedicated perception encoder, the model accepts interleaved text and images. This enables agents to interpret screenshots, charts, and documents alongside conversation.
- Scaffold Compatibility. Muse Glimmer works across OpenClaw and other agentic orchestration patterns.
- Controllable Effort. Muse Glimmer supports different reasoning strengths to select the right balance between quality and speed.
- Multilingual. Muse Glimmer is trained on data from more than 100 languages.
Performance
We evaluated Muse Glimmer across a broad range of benchmarks to assess the diverse capabilities required for effective autonomous agent behaviour. Compared with Gemma4-31B and Qwen3.6-27B, Muse Glimmer performs strongly for its size class on several widely used LLM benchmarks.
Optimised for local deployments
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. Meta applied two optimisations to make Muse Glimmer run at practical speeds on consumer hardware without sacrificing quality.
Fitting the model on your device
At full precision, a 30-billion parameter model would require over 55GB of memory – far more than any consumer GPU offers. Meta uses quantisation techniques to compress the model’s weights to approximately 4-bit precision, shrinking the language model to under 20GB. 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 24GB or 32GB envelope. It validated that this compression introduces minimal to no degradation on agentic tasks.
Faster generation through speculative decoding
Language models normally generate text one token at a time which can feel slow during long reasoning chains or multi-step tool calls. Muse Glimmer ships with a lightweight “drafter” model based on DFlash – a small companion network that proposes entire blocks of tokens at once. The main model then verifies these proposals in parallel, accepting correct tokens and correcting wrong ones. This technique lets Muse Glimmer generate text significantly faster than standard token-by-token generation while producing identical output quality. It provides quantised drafter versions to incur a smaller memory overhead in the release.
The result:
Meta measured the speed of its K-Quant-17GB model alongside the quantised DFlash drafter on MacBook M4-Max, M5-Max and on a RTX-5090. The model is fast enough for fluid conversation and realtime agent interaction, all running entirely on your device.
Available today
Muse Glimmer is available now, and you can download the weights on Hugging Face. In the coming days, run it locally through partners like Ollama, LM Studio, and Unsloth, deploy it with edge frameworks including llama.cpp, ExecuTorch, and MLX, serve it at scale with vLLM and SGLang, or get started quickly through partners like Together AI, Fireworks AI, and OpenRouter. You can even customise it for your use-case by leveraging PyTorch’s TorchTitan training feature to customise the model further.
In addition, we’re releasing documentation so developers have the resources they need to get started and build responsibly with Muse Glimmer. This includes guidance on setting up custom scaffolds, so it’s even easier to start building and deploying personal agents on day one. You can learn more and find resources to build on Meta’s AI Developer Center.