How to Autostart MiniMax-M2.5

The fastest way to get this model running locally is via Docker.

Just follow the guidelines provided below.

The setup auto-downloads all needed files (several GBs).

You don’t need to tweak anything, as the installer will automatically pick the highest performing setup for you.

🧩 Hash sum → a4204c447a4672113e0432d12262a168 — Update date: 2026-06-22 Verify Processor: Intel i5 or AMD Ryzen 5 for basic 7B models RAM: 32 GB highly recommended for 26B+ GGUF models Disk Space: 100 GB for multi-modal model vision components Graphics: TensorRT-LLM / vLLM inference engine compatible chip

MiniMax-M2.5 is an next‑generation transformer-based AI model designed for both textual and visual tasks. It leverages a sparse attention mechanism to achieve high inference speed while maintaining state‑of‑the‑art accuracy across benchmarks. The architecture incorporates a mixture‑of‑experts routing strategy, allowing efficient scaling to 175 billion parameters without a proportional increase in computational cost. Its training pipeline utilizes a curated web‑scale corpus combined with multimodal datasets, enabling robust context understanding and generation in multiple languages. The model’s energy‑efficient design reduces inference latency, making it suitable for deployment on edge devices and cloud services alike. Below is a concise comparison of key technical specifications:

1. Installer deploying automated RAG data chunking pipelines for multi-format text catalogs trees
2. Launch MiniMax-M2.5 Complete Walkthrough
3. Setup utility automating model conversion from PyTorch to GGUF
4. MiniMax-M2.5
5. Downloader for optimized bitsandbytes 4-bit model weights
6. MiniMax-M2.5 Locally via Ollama 2 Windows