Muse Glimmer – Meta's Open-Source 30B-Parameter Large Language Model Operational

Muse Glimmer is Meta's open-source 30-billion-parameter large language model, deeply optimized for always-on local Agent workflows. Using 4-bit quantization, the model runs smoothly on a single GPU with 24 GB of VRAM or on Apple Silicon, with minimal inference performance degradation. Tests show that the model handles real-time conversation and Agent interactions on the RTX 5090 and M4/M5 Max platforms. Combined with DFlash speculative decoding, it delivers up to 3.1× faster performance and leads similarly sized competitors across benchmarks including MCP Atlas and SWE-Bench Pro.

Local Agent inference: Supports always-on background operation and is deeply optimized for Agent workflows, completing complex task orchestration without an internet connection.
Efficient deployment with low VRAM requirements: With 4-bit quantization, the 30B-parameter model runs locally on a single GPU with 24 GB of VRAM or a Mac with 32 GB of unified memory.
Real-time interactive conversation: Delivers smooth conversation and real-time Agent responses on M4 Max, M5 Max, and high-end PCs for low-latency scenarios.
Multimodal capabilities and tool calling: Supports Agent benchmarks such as MCP Atlas and provides tool use, code generation, and multi-step reasoning capabilities.

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Model architecture and scale: Muse Glimmer is based on the Transformer architecture and has 30 billion total parameters. Post-training and instruction tuning for Agentic tasks strengthen its tool-calling, code-generation, and multi-step reasoning capabilities.
4-bit quantization: Low-bit quantization substantially compresses the model, allowing the 30B-parameter model to run locally on a single GPU with 24 GB of VRAM or a Mac with 32 GB of unified memory, with negligible measured performance degradation on Agent tasks.
DFlash speculative decoding: The model integrates speculative decoding, using a draft model to quickly generate candidate tokens and the main model to verify them in parallel. It achieves up to 3.1× faster performance on the RTX 5090, reaching a decoding speed of 233 tok/s.
Cross-platform inference frameworks: ExecuTorch is used for Apple Silicon, while llama.cpp is used for NVIDIA GPUs, ensuring a consistent and efficient local inference experience across hardware platforms.

Prepare the environment: Ensure the local device has an NVIDIA GPU with at least 24 GB of VRAM or an Apple Silicon Mac with 32 GB of unified memory (M4/M5 Max recommended).
Download the weights: Visit the Hugging Face repository meta-models/Muse-Glimmer-30B and download the 4-bit quantized model files and configuration files.
Choose a framework: NVIDIA users can load the model through llama.cpp; Apple Silicon users can run it with ExecuTorch for optimal performance.
Start inference: Load the model with a compatible local inference UI and enable DFlash speculative decoding for the highest inference speed.
Connect to Agent workflows: Connect the model to personal knowledge bases, code repositories, and toolchains through the MCP protocol or a local API to enable always-on background task automation.

Exceptional local performance: After 4-bit quantization, performance degradation is negligible, while the model substantially outperforms Gemma4-31B and Qwen3.6-27B on tasks including SWE-Bench Pro and DeepSearch QA.
Accessible hardware requirements: Breaks the VRAM barrier for local deployment of large models. With 24 GB of VRAM, it can run a 30B-parameter model, reducing costs for individual developers and small and medium-sized businesses.
Major inference speed gains: DFlash speculative decoding increases decoding speed on the RTX 5090 from 74.9 tok/s to 233 tok/s, achieving 3.1× acceleration.
Security and control: With 100% local computation, data never leaves the device. In Siren AgentDojo security tests, its attack success rate was lower than that of competitors, balancing performance and privacy.

Hugging Face model repository: https://huggingface.co/meta-models/Muse-Glimmer-30B

Local AI assistant: As an always-on personal Agent, it handles everyday tasks such as schedule management, email drafting, and code review without relying on cloud APIs.
Privacy-sensitive industries: Suitable for fields such as healthcare, finance, and law with stringent data-compliance requirements, ensuring that core data remains 100% on local devices.
Developer toolchains: Integrates into IDEs or terminals to provide offline code completion, automated test generation, and repository-level code understanding, supporting software engineering tasks at the SWE-Bench level.
Edge-device deployment: Runs on workstations with 24GB+ of VRAM or high-performance Macs, providing small and medium-sized teams with low-cost, enterprise-grade AI capabilities.