How to Launch MiniMax-M2.5 Quantized GGUF Windows

Deploying this model locally is quickest when done via Docker.

Please follow the instructions listed below to get started.

The installer auto-downloads and deploys the entire model pack.

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

🔍 Hash-sum: 1327385b820b8e81e41fa691aa5410e0 | 🕓 Last update: 2026-06-23 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: high-speed DDR5 memory preferred for CPU offloading Disk: 150+ GB for high-context vector database storage GPU: modern architecture (Ada Lovelace / Ampere minimum)

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:

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