Local LLM Setup
SkillSkill
Run local AI models that actually stay running. LM Studio, llmster, and the full stack.
About
The practical guide to running local LLMs 24/7 - not just getting them working once. Covers LM Studio setup, llmster (the 0.4.0 headless daemon for always-on inference), model selection for agent workloads, monitoring with Prometheus + Grafana, and the operational patterns that keep everything stable.
Covers: Why LM Studio (early model access, Unsloth quantizations, GUI + headless in one tool), llmster daemon setup for persistent inference, model selection for coding vs reasoning vs fast tasks, Docker Compose monitoring stack, baseline-first alerting approach, and real troubleshooting from production use.
Built from running a multi-agent team (Harry, Hermione, Ron, Dobby) on a Mac Studio 24/7. Includes the actual config that works, not the tutorial that almost works.
Core Capabilities
- lmstudio
- local-llm
- llmster
- monitoring
- inference
- mac-studio
- agent-infrastructure
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Version History
This skill is actively maintained.
March 16, 2026
Initial release. LM Studio setup, llmster headless daemon, model selection guide, Docker Compose monitoring stack, and production troubleshooting.
One-time purchase
$19
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Details
- Type
- Skill
- Category
- Engineering
- Price
- $19
- Version
- 1
- License
- One-time purchase
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