Install MiniMax-M2.5 with 1M Context For Beginners

The shortest path to running this model is by activating Hyper-V features.

Follow the sequence of steps detailed below.

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

The automated script takes care of everything, tailoring the setup to your specs.

🧾 Hash-sum — 24254d9ba4ef40206ebe8029bcf1147a • 🗓 Updated on: 2026-06-26

CPU: multi-threading optimized for fast prompt processing
RAM: 64 GB to avoid OOM crashes on large contexts
Disk Space: 80 GB NVMe SSD required for fast model weights loading
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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