This model requires a specialized High-VRAM environment. Ensure you have the latest CUDA Drivers or Metal Framework installed.
Minimum VRAM: 87GB VRAM Recommended
Origins & History
The minimax-m2 model by MiniMax is a 230B (MoE) parameter architecture optimized for code tasks. It requires approximately 85GB of VRAM to comfortably run locally using a Q4_K_M quantization. Extending the context window up to 131,072 tokens will dynamically allocate further VRAM, meaning high-bandwidth memory hardware is strictly advised.
Pros
Full privacy and offline inference capabilities
Highly capable 230B (MoE) parameter structure
Supports impressive 131,072 token context window
Cons
Requires 85GB+ VRAM minimum
Local inference speed depends entirely on memory bandwidth (GB/s)
Architect's Runtime Strategy
For running minimax-m2 at maximum tokens-per-second, we recommend using LM Studio or Ollama with a GGUF quantization (Q4_K_M or Q6_K). If you are multi-GPU, use vLLM to distribute the layers across your VRAM pool for optimal throughput.
Common Questions
What hardware do I need to run minimax-m2?
You will need a GPU with at least 87GB of VRAM to run the Q4_K_M quantized version smoothly with a moderate context window.
How do I install minimax-m2 locally?
The simplest method is utilizing Ollama by executing 'ollama run minimax-m2' directly in your command line. Alternatively, you can search for the model via LM Studio's interface.