Summary
Keith AI delivers a detailed, framework-driven evaluation of running Hermes and OpenClaw locally on a Beelink S10 Max mini PC — the dedicated OpenClaw Edition variant — motivated by cloud API costs that had reached $10–20 per day. Before diving into hardware, he lays out seven decisions every local AI stack requires: where the agent process runs, where the LLM inference runs, hardware class, model selection, runtime choice, acceptable tokens-per-second threshold, and privacy requirements. The framework alone makes this video useful for anyone planning a local deployment from scratch.
The Beelink S10 Max OpenClaw Edition runs an AMD Ryzen AI 9 chip with 64GB RAM and 1TB storage, and Keith benchmarks Qwen 3.6 9B via llama.cpp at approximately 12 tokens per second — sufficient for simple tasks but noticeably slow when Hermes orchestrates complex tool-call chains or spawns sub-agents. VLLM was tested and found too difficult to configure and too slow on consumer-class hardware; llama.cpp offered the best performance-to-effort ratio. Tailscale connects his MacBook and iPhone to the Beelink over a private network, enabling remote access without exposing the device publicly.
The honest assessment is that local is not free: there are real latency trade-offs, hardware costs, and setup complexity. Keith’s recommended architecture is hybrid — route privacy-sensitive tasks (API keys, personal data, regulated information) through the local Beelink, and offload complex non-sensitive tasks to cloud models when speed matters more than data sovereignty. This is a particularly practical guide for teams in regulated industries weighing GDPR or data-residency requirements against frontier model performance.
📺 Source: Keith AI · Published May 13, 2026
🏷️ Format: Workflow Case Study
