Run Muse Glimmer for Local Vibe Coding with llama.cpp, DFlash, and Pi

Muse Glimmer is gaining attention in the local AI community and is being compared with Qwen’s 27B-class models. In many cases, it is performing better, especially for local coding and agentic workflows.

Meta looks strong in the open-model space, and with a few more iterations, models like this could start competing closely with proprietary systems. As an AI enthusiast, it’s exciting to be able to run this level of AI locally.

In this guide, we will run Muse Glimmer with llama.cpp, speed it up with DFlash, and connect it to Pi for local vibe coding. It will be able to build, test, and debug a project directly from the terminal.

1. Downloading Muse Glimmer

First, download the main Muse Glimmer model and its DFlash drafter from Hugging Face.

Install the Hugging Face CLI:

curl -LsSf  | bash
echo 'export PATH="/root/.local/bin:$PATH"' >> ~/.bashrc
source ~/.bashrc

Log in:

hf auth login

Create a model directory:

mkdir -p /workspace/muse-glimmer

Download the 16.8 GB main model:

hf download meta-models/Muse-Glimmer-30B-GGUF 
  muse-glimmer-30B-kquant-17gb.gguf 
  --local-dir /workspace/muse-glimmer

Download the 1.63 GB DFlash drafter:

hf download meta-models/Muse-Glimmer-30B-GGUF 
  dflash-kquant.gguf 
  --local-dir /workspace/muse-glimmer

Both files will be saved in /workspace/muse-glimmer.

2. Installing and Running llama.cpp

Next, install llama.cpp with CUDA support and use it to serve Muse Glimmer with the DFlash drafter.

Install and build llama.cpp:

cd /workspace

git clone 
cd llama.cpp
git pull origin master
cmake -B build -DGGML_CUDA=ON -DCMAKE_BUILD_TYPE=Release
cmake --build build --config Release -j$(nproc)
ln -sf "$(pwd)/build/bin/llama-server" /root/.local/bin/llama-server

Check the installation:

git pull origin master
cmake -B build -DGGML_CUDA=ON -DCMAKE_BUILD_TYPE=Release
cmake --build build --config Release -j$(nproc)
ln -sf "$(pwd)/build/bin/llama-server" /root/.local/bin/llama-server

You should see the installed llama.cpp version and build information.

Now start Muse Glimmer with DFlash speculative decoding:

llama-server 
  -m /workspace/muse-glimmer/muse-glimmer-30B-kquant-17gb.gguf 
  -md /workspace/muse-glimmer/dflash-kquant.gguf 
  --spec-type draft-dflash 
  --spec-draft-n-max 15 
  -ngl all 
  --spec-draft-ngl all 
  -fa on 
  --ctx-size 16384 
  --alias muse 
  --host 0.0.0.0 
  --port 8080 
  --jinja

Here, llama.cpp loads the main model and the separate DFlash drafter onto the GPU, using speculative decoding to improve generation speed.

3. Testing Muse Glimmer

Once the server is running, you can test Muse Glimmer directly through the built-in llama.cpp Web UI.

Open:

In my initial testing, I was getting around 46 tokens/second, which is already quite good.

During longer coding tasks, I saw speeds reach around 127 tokens/second, making the model feel much faster for agentic coding workflows.

The results were mixed, though. Muse Glimmer created an HTML game for me, but it didn’t work particularly well. For this kind of task, I still found Qwen3.8-27B noticeably better at producing working HTML apps and games.

4. Installing Pi Coding Agent

Next, install Pi and connect it to the llama.cpp server running Muse Glimmer.

Install Pi:

curl -fsSL  | sh

Then install Hugging Face’s llama.cpp extension:

pi install git:github.com/huggingface/pi-llama

Restart your terminal after installation.

The pi-llama extension automatically connects to:

v1

It detects the models being served by llama.cpp, so you do not need to configure models.json manually.

5. Starting Local Vibe Coding

Now create a project and select Muse Glimmer as the model inside Pi.

Create an empty project:

mkdir -p /workspace/glimmer-test
cd /workspace/glimmer-test

Launch Pi:

pi

Inside Pi, run:

/model

Search for:

llama-cpp

Then select:

muse

Muse Glimmer should now be available through Pi’s llama-cpp provider.

6. Testing Muse Glimmer as a Coding Agent

Finally, give Muse Glimmer a complete coding task and let it build, test, and debug the project itself.

I used this prompt:

 

Build a complete Python task management API from scratch using FastAPI.

Requirements:
– Create a clean project structure.
– Add endpoints to create, list, update, and delete tasks.
– Use SQLite for persistence.
– Add input validation and error handling.
– Add pytest tests for all endpoints.
– Create requirements.txt and README.md.
– Run the tests yourself.
– Fix any errors and rerun the tests until everything passes.

Do not ask me to create files or run commands for you. Build and test the complete project yourself.

 

Muse Glimmer built the project in around 2 minutes.

To test it locally:

pip install -r requirements.txt
uvicorn app.main:app --reload

Open the API documentation at:

Instead of manually testing every endpoint, I also asked Muse Glimmer to test the complete API itself and give me a final report.

For local agentic coding, this is where Muse Glimmer impressed me most. It was fast, handled multi-step tasks well, and took only a few seconds to identify and fix issues during debugging.

Final Thoughts

Muse Glimmer is a good indicator of how far local AI coding has come, especially when Meta provides the official model files and recommended configuration. For me, it was very easy to set up and start using.

There are still a few rough edges, but as Muse Glimmer, llama.cpp, DFlash, and the surrounding tooling matures, I expect better results, faster speeds, and stronger agentic coding performance locally.

If you have an RTX 3090, 4090, or 5090, I would highly recommend trying either Muse Glimmer or Qwen3.8 locally. At this point, it is becoming harder to justify paying for every AI coding request or sharing your code and data with third-party services.

Local models are already getting surprisingly close to the experience of models like GLM-5.2, and I think the next few iterations will make local AI coding even more compelling.

 
 

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in technology management and a bachelor’s degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.