9 July 2026 | Chunkers

Install MiniMax-M2.5 with 1M Context

Deploying locally takes the least amount of time when executed through native OS tools.

Use the instructions provided below to complete the setup.

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

The initial setup handles the heavy lifting, fine-tuning the environment for your device.

🔧 Digest: c9db12f1f6b3af7fa369efe0b007b471 • 🕒 Updated: 2026-07-03
  • Processor: 6-core 3.5 GHz minimum required
  • RAM: 48 GB needed to prevent memory swapping to disk
  • Disk Space: at least 100 GB for multiple local LLM variants
  • GPU: modern architecture (Ada Lovelace / Ampere minimum)

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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