Meta releases Muse Glimmer, a 30B open-weight multimodal model optimized for local agent workflows, with permissive Apache 2.0 licensing, 4-bit quantization support, speculative decoding, and broad ecosystem integrations.
Hi r/LocalLLaMA 👋 Today we’re excited to release Muse Glimmer, a 30B open-weight model built specifically for local agent workflows. We’re releasing the weights to the community under a permissive Apache 2.0 license. A few specs 30B params, dense Multimodal: interleaved text + images via a dedicated perception encoder Trained on 100+ languages Controllable reasoning effort (quality/speed tradeoff) Memory footprint At full precision, 30B needs 55+ GB, which is out of reach for consumer hardware. We quantize weights to ~4-bit, bringing the LM under 20 GB. That leaves headroom in a 24 GB or 32 GB envelope for the KV cache, the perception encoder, and the speculative decoding drafter running simultaneously. We validated minimal to no degradation on agentic tasks under compression. Speculative decoding Ships with a lightweight DFlash-based drafter that proposes blocks of tokens which the main model verifies in parallel. Significantly faster than token-by-token generation with identical output quality. We're also shipping quantized drafter versions so the memory overhead stays small. A few capabilities We trained Muse Glimmer for agentic loop tasks, including: End-to-end task completion (strong performance on DeepSearch QA, MCP-Atlas, 𝛕3-Bench, SWE-Bench, and more) Function calling with precise schemas across long workflows Multi-step reasoning over long horizons Failure recovery — when a tool call fails or returns something unexpected, it's trained to diagnose and retry instead of halting. This was a deliberate training target. Works with OpenClaw and other agentic scaffolds Multimodal understanding and reasoning Running it Weights are up on Hugging Face. Coming soon: Ollama, LM Studio, Unsloth and torchtitan, plus optimized integrations for llama.cpp, MLX, and ExecuTorch. vLLM and SGLang for serving. Get started quickly with Together AI, Fireworks AI, and OpenRouter. We're also working with AMD, Arm, Dell, Intel, and NVIDIA on per-device optimization. We look forward to your feedback and seeing what the community builds with Muse Glimmer. 🔗 Weights: https://huggingface.co/meta-models 🔗 Research Blog: https://go.meta.me/museglimmer 🔗 Resources: https://developer.meta.com/ai/models/muse-glimmer/
Meta introduces Muse Glimmer, a new 30B open-weights model under Apache 2.0, optimized for agentic task completion, reliable tool use, and multi-step reasoning. Simon Willison tests it locally with LM Studio and llm-coding-agent.
Meta introduces Muse Glimmer, a permissively licensed 30B-parameter model optimized for local agent workflows, coding, and tool use, with weights released on Hugging Face.
Meta released Muse Glimmer, a 30B multimodal agentic model under Apache 2.0, designed for local deployment with day-0 support across Hugging Face libraries.
Meta introduced Muse Glimmer, an open-weight 30B-parameter model distilled from Muse Spark for on-device agentic workflows, with ExecuTorch now supporting running it on NVIDIA GPUs and Apple silicon.
Meta Superintelligence Lab releases Muse Glimmer 30B in GGUF quantized formats for local inference, a distilled causal language model with a perception encoder for autonomous agentic tasks, tool use, multimodal input, and failure recovery on consumer hardware.