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How my AI Agent views and maintains "our" homelab

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Open-source models provide complete access to the entire model architecture, training methodology, and weights. This comprehensive access includes the model code, architecture design, training scripts, and parameter weights under licenses like MIT or Apache.

Open-weight models represent a more limited approach to model sharing. These models release only the trained parameter weights while restricting access to training methodologies and code. Also under MIT or Apache licenses

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How my AI Agent views and maintains "our" homelab

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my sudoers file restricts what the llm can actually do, also I have robust backups can can spin up any of my servers really quickly, I am not that worried and just like you deal with human errors, you can deal with agent errors.

so far this has been running for a month, no scares or unexpected behaviour other than looping on a task somethimes

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llama.cpp Multi-Model Server Architecture: ASUS Zenbook UM3504DA

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Nice write-up. Thanks for also including all the numbers. If I might ask: What is the thermal/throttling behaviour you mention? Is it still within the laptop’s thermal budget? Or does it reach throttling territory when doing inference on a long context window?

Thanks! Nowhere near throttling territory. The thermal ceiling is 95°C, and my sustained load is 72-78°C. There's a 17-23°C safety margin.

The laptop never throttles. But the fans also don't spin up properly during GPU-heavy inference unless I force it.

Why? ASUS's embedded controller monitors CPU temperature and CPU package power for fan curves. It largely ignores GPU temperature.