Launch MiniMax-M2.5 on AMD/Nvidia GPU
The shortest path to running this model is by activating Hyper-V features.
Refer to the action plan below to initialize the model.
The tool automatically synchronizes and downloads the model database.
The setup file includes a feature that instantly optimizes all configurations.
MiniMax-M2.5 is an nextâgeneration transformer-based AI model designed for both textual and visual tasks. It leverages a sparse attention mechanism to achieve high inference speed while maintaining stateâofâtheâart accuracy across benchmarks. The architecture incorporates a mixtureâofâexperts routing strategy, allowing efficient scaling to 175âŻbillion parameters without a proportional increase in computational cost. Its training pipeline utilizes a curated webâscale corpus combined with multimodal datasets, enabling robust context understanding and generation in multiple languages. The modelâs energyâefficient 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 | 175âŻB |
| Context Length | 8K tokens |
| Training Data Size | 1.5âŻTB |
| Inference Speed | >200âŻtokens/s |
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