LM Studio powers OpenClaw local stack

This is the right move if the goal is turning per-call AI spend into a mostly fixed hardware bill. The catch is that local inference only feels cheap when the model stays warm and the network path stays boring.

• –LM Studio's OpenAI-compatible server means most agent apps can swap in local inference with a `base_url` change instead of a custom integration.
• –OpenClaw already documents LM Studio support and recommends a full-size MiniMax M2.1 build, which is a strong sign the stack is meant to work in practice.
• –A 4090 and 64GB of RAM should make mid-sized local models practical enough to test agent workflows without watching every request burn dollars.
• –The real tradeoff is ops discipline: keep the model loaded, keep latency predictable, and make sure the server-to-server link is secure and reliable.
• –For many developers, local models will be the "good enough" path for routine agent work, while hosted frontier models stay on call for the hard stuff.