How to Run Muse Glimmer Locally

From download to first response on your own hardware. Every step, every GPU option, every quantization tradeoff.

Hardware Requirements

Every GPU tier from budget to optimal — pick the row that matches your machine.

Tier GPU RAM Notes
Minimum (quantized) RTX 4060 8GB / RX 7600 8GB 16GB Q4 quantization, basic inference, slower
Recommended RTX 4070 12GB / RX 7800 XT 16GB 32GB Q5/Q6 quant, comfortable speed for coding and chat
Optimal RTX 4090 24GB / RX 7900 XTX 24GB 32GB+ Full or near-full precision, fast agentic workflows
AMD Ryzen AI Max+ Integrated (up to 128GB unified) Unified memory AMD-optimized path, runs without discrete GPU
Mac (Apple Silicon) M2 Pro+ / M3+ / M4+ 32GB+ unified MLX or llama.cpp, good speed on 32GB+ configs

Step-by-Step Setup

Four steps from zero to running a frontier model on your own machine.

1

Install Ollama

Download Ollama from ollama.com — one installer for macOS, Linux, and Windows. It manages model downloads, quantization, and serving automatically.

2

Pull Muse Glimmer

Run: ollama pull muse-glimmer. Ollama downloads the right quantization for your hardware. On a 12GB GPU, expect a Q5 quant around 20GB.

3

Start chatting

Run: ollama run muse-glimmer. That is it — you are running a 30B frontier model locally. No API key, no cloud account, no internet required after download.

4

Connect to your tools

Point Cursor, Continue, VS Code, or any OpenAI-compatible client at http://localhost:11434/v1. Muse Glimmer becomes your private coding assistant.

Running Muse Glimmer — FAQ

What is the easiest way to run Muse Glimmer?
Install Ollama (one command), then run: ollama pull muse-glimmer && ollama run muse-glimmer. It handles quantization, memory management, and serving automatically.
Can I run it on an NVIDIA GPU?
Yes — any NVIDIA GPU with 8GB+ VRAM and CUDA support. RTX 30/40/50 series all work. Ollama and llama.cpp auto-detect NVIDIA GPUs.
Can I run it on an AMD GPU?
Yes — AMD co-launched with Meta. Radeon RX 7000 series and Ryzen AI Max+ are officially supported via ROCm. Ollama has ROCm backend support.
What about Apple Silicon Macs?
M2 Pro and above with 32GB+ unified memory run it well via MLX or llama.cpp. M4 Macs are particularly fast thanks to the neural engine acceleration.
How much disk space do I need?
About 20GB for Q5 quantization, 16GB for Q4. The full BF16 weights are larger but most users should use quantized versions for local inference.

Curious how it performs after setup?

View Benchmarks Compare vs Other Models AMD Hardware Guide