MiniMax-M2.5 on AMD/Nvidia GPU For Low VRAM (6GB/8GB) Local Guide Windows

The fastest method for installing this model locally is by using Docker.

Check out the detailed setup guide below to begin.

The installer automatically pulls the model (could be multiple GBs).

Once launched, the wizard detects your specs to configure the model for maximum efficiency.

📤 Release Hash: 7b6aa1d68626ab7077df7fe2513c49c1 • 📅 Date: 2026-06-30



  • CPU: AVX2/AVX-512 instruction set required for llama.cpp
  • RAM: 32 GB or higher for smooth 32k context lengths
  • Disk Space: 80 GB NVMe SSD required for fast model weights loading
  • GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference

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:

SpecValue
Parameter Count175 B
Context Length8K tokens
Training Data Size1.5 TB
Inference Speed>200 tokens/s
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