Deploy MiniMax-M2.5 No Admin Rights Offline Setup Windows

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

Refer to the instructions below to proceed.

1-click setup: the app automatically fetches the large weight files.

An automated hardware sweep ensures the system will select the best tuning parameters.

📄 Hash Value: 096f0fff2984e85816f4b145e79f48b9 | 📆 Update: 2026-06-29



  • CPU: 8-core / 16-thread recommended for orchestration
  • RAM: enough space for background apps and OS overhead
  • Storage:100 GB free space for HuggingFace cache folder
  • Graphics: CUDA Compute Capability 8.0+ required for flash-attention

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:

Spec Value
Parameter Count 175 B
Context Length 8K tokens
Training Data Size 1.5 TB
Inference Speed >200 tokens/s
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