Descriptions:
Fahd Mirza walks through how to run the Qwen3.5 35B model entirely locally using llama.cpp and then integrate it with OpenClaw, Anthropic’s open-source command-line agent framework — no API keys or cloud services required. The setup runs on an NVIDIA RTX 6000 GPU with 48GB of VRAM, drawing around 36.45GB during operation, giving a realistic picture of the hardware required for a model at this scale.
The tutorial covers the full installation sequence: setting up Node.js and npm via nvm, installing OpenClaw and running its onboarding process, and then editing OpenClaw’s configuration file to point its provider at the locally running llama.cpp server on port 8080. The key configuration elements — provider name, base URL, API completion path, and model ID — are shown directly, and Mirza provides the complete config file and command list in a pinned comment for easy reproduction.
Once the gateway service is started, the Qwen3.5 35B model becomes accessible through OpenClaw and can be connected to external channels such as Telegram. This makes the tutorial relevant for developers looking to self-host capable open-weight models with a feature-rich agent interface, avoiding recurring inference costs while maintaining full control over their data and infrastructure.
📺 Source: Fahd Mirza · Published February 25, 2026
🏷️ Format: Tutorial Demo
