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cturan/
MiniMax-M2-GGUF

Name: MiniMax-M2-GGUF
Availability: InStock

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Building and Running the Experimental `minimax` Branch of `llama.cpp`

Note:
This setup is experimental. The minimax branch will not work with the standard llama.cpp. Use it only for testing GGUF models with experimental features.

System Requirements (you can use any supported this is for ubuntu build commands)

Ubuntu 22.04
NVIDIA GPU with CUDA support
CUDA Toolkit 12.8 or later
CMake

Installation Steps

1. Install CUDA Toolkit 12.8

bash

wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
sudo dpkg -i cuda-keyring_1.1-1_all.deb
sudo apt-get update
sudo apt-get -y install cuda-toolkit-12-8

2. Set Environment Variables

bash

export CUDA_HOME=/usr/local/cuda
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64
export PATH=$PATH:$CUDA_HOME/bin

3. Install Build Tools

sudo apt install cmake

4. Clone the Experimental Branch

bash

git clone --branch minimax --single-branch https://github.com/cturan/llama.cpp.git
cd llama.cpp

5. Build the Project

bash

mkdir build
cd build
cmake .. -DLLAMA_CUDA=ON -DLLAMA_CURL=OFF
cmake --build . --config Release --parallel $(nproc --all)

Build Output

After the build is complete, the binaries will be located in:

llama.cpp/build/bin

Running the Model

Example command:

./llama-server -m minimax-m2-Q4_K.gguf -ngl 999 --cpu-moe --jinja -fa on -c 32000 --reasoning-format auto

This configuration offloads the experts to the CPU, so approximately 16 GB of VRAM is sufficient.

Notes

--cpu-moe enables CPU offloading for mixture-of-experts layers.
--jinja activates the Jinja templating engine.
Adjust -c (context length) and -ngl (GPU layers) according to your hardware.
Ensure the model file (minimax-m2-Q4_K.gguf) is available in the working directory.

All steps complete. The experimental CUDA-enabled build of llama.cpp is ready to use.

cturan/MiniMax-M2-GGUF

Building and Running the Experimental minimax Branch of llama.cpp

System Requirements (you can use any supported this is for ubuntu build commands)

Installation Steps

1. Install CUDA Toolkit 12.8

2. Set Environment Variables

3. Install Build Tools

4. Clone the Experimental Branch

5. Build the Project

Build Output

Running the Model

Notes

cturan/
MiniMax-M2-GGUF

Building and Running the Experimental `minimax` Branch of `llama.cpp`