Local Deployment: Running LongCat-2.0 using Meituan Inferenceget Code

Hardware & Requirements

• Architecture (MoE): Mixture-of-Experts architecture requiring high memory bandwidth
• Parameter Count: 1.6 Trillion total / 48 Billion active per token
• VRAM Requirement: Must comply with recommended amount for specific quantization levels provided in valid configurations
• Context Window Support: Up to 1 million tokens out of the box certain GPU generations required or higher vram capacity preferred

Installation & Launch Guide

1. Prepare your environment compatible with GitHub source code from meituan-longcat/LongCat-2.0
2. Download weights directly from Hugging Face huggingface.co/meituan-longcat/LongCat-2.0 instead of relying on cloud APIs any way you wish
3. Configure inference engine according to approved settings such even if running via automated catalogs like Immers Foundation Models which provides ready configs

# Example workflow logic 
$ git clone https://github.com/meituan-longcat/LongCat-2.0 
$ # Download weights and configure hardware parameters before execution

Optimization & Performance Tips

• Verify context window limits as this model supports up to a large scale (upto 1M own) but requires sufficient VRAM monitoring checking manual validation rules
• Monitor concurrent request counts based on available server resources properly configured through specialized service providers who handle equipment setup automatically
• Utilize agentic workflows by connecting specifically to Claude Code, OpenClaw, or Hermes Agent for better task automation performance

Bottom Line: Deploying LongCat-2.0 locally allows full commercial use under MIT license without token payment restrictions while maintaining privacy.

! DYOR (Do Your Own Research)