MiniMax-M2.5 Using Pinokio Quantized GGUF 5-Minute Setup
30 Giugno 2026
For the fastest local setup of this model, enabling Windows Features is best.
Follow the sequence of steps detailed below.
The download manager will automatically pull several gigabytes of data.
The initial setup handles the heavy lifting, fine-tuning the environment for your device.
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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