Its performance outperforms that of 235B, enabling single-card privatized deployment of OpenClaw

Complete Deployment Guide for a Local AI Agent Platform Based on Docker + llama.cpp This solution has been validated on a single GPU with 22GB VRAM (e.g., RTX 2080 Ti), achieving an optimal balance between performance and functionality. It is well-suited for private AI agent scenarios requiring long context, low concurrency, and high accuracy. Table of Contents Solution Overview Deploying llama.cpp Local Model Service OpenClaw Deployment Guide Common Issues and Notes Summary and Recommendations Preface Why Choose Local Deployment Over Cloud APIs? Advantage Description Data Security All project code, files, and interaction records remain within your internal network, preventing sensitive data leakage. Cost Control Eliminates expensive token-based cloud fees—especially beneficial for high-context, high-interaction platforms like OpenClaw. Full Autonomy Enables free selection of open-source models and full customization of context length, concurrency, quantization precision, and more. Why Qwen3.5 Series Models? Qwen3.5 adopts a hybrid architecture that effectively addresses inference bottlenecks in…