이미지: METAL LAB 생성
Summary
- Meta AI has released "Muse Glimmer," a 30-billion-parameter model for local agent workflows, under the Apache 2.0 license
- 4-bit quantization shrinks the language model's footprint to under 20GB, making it runnable on 24GB and 32GB-class consumer GPUs
- The model was trained for agent-loop tasks including DFlash-based speculative decoding, function calling, and failure recovery
- 모델명
- Muse Glimmer
- 파라미터 규모
- 300억(30B), 밀집형(dense)
- 라이선스
- Apache 2.0
- 메모리 요구
- 전체 정밀도 55GB 이상 → 4비트 양자화 시 20GB 미만
- 지원 언어
- 100개 이상
- 제공 채널
- Hugging Face, 향후 Ollama·LM Studio·Unsloth·vLLM·SGLang 등
Meta AI has released "Muse Glimmer," a 30-billion-parameter open model specialized for local agent workflows. According to an announcement posted to r/LocalLLaMA, the model's weights have been released to the community under the Apache 2.0 license.
Muse Glimmer is a multimodal model with a dedicated perception encoder that interleaves text and images, and it was trained on more than 100 languages. It also includes an adjustable reasoning-effort feature that lets users balance quality against speed.
At full precision the model requires more than 55GB of memory, making it difficult to run on consumer hardware, but 4-bit quantization brings the language model's own footprint below 20GB. Meta said this leaves enough headroom to run the KV cache, the perception encoder, and the speculative-decoding drafter simultaneously on a 24GB or 32GB-class GPU. The company added that it observed little degradation in agent-task performance even after compression.
The model ships with a lightweight DFlash-based drafter that supports speculative decoding, proposing candidate token blocks that the main model verifies in parallel. Meta said it validated performance on benchmarks including DeepSearch QA, MCP-Atlas, and SWE-Bench, and that a "failure recovery" capability — diagnosing and retrying after failed tool calls — was also a training objective.
The weights have been released on Hugging Face, with integrations for Ollama, LM Studio, Unsloth, torchtitan, and optimizations for llama.cpp, MLX, and ExecuTorch coming soon. Serving support via vLLM and SGLang is also provided, along with access through Together AI, Fireworks AI, and OpenRouter. Meta added that it is also working with AMD, Arm, Dell, Intel, and NVIDIA on device-specific optimizations.
