I’m Obsessed With Local AI. Here’s Why

Introduction: Explains local AI—models running on hardware you control—versus cloud AI, and provides a practical map for builders. Covers model families (notably Gemma), model discovery (Hugging Face), runtimes (LM Studio, Ollama, Google AI Edge), quantization, hardware trade-offs, and hybrid local/cloud patterns. Concludes with three concrete startup workflows.

– When local matters: private data, offline or field work, low latency, and high-frequency internal tasks.
– How to run: read model cards, try LM Studio or Ollama, use GGUF or Light RTLM for deployment; match model size to hardware.
– Business examples: home‑health QA reviewer; restoration field report co‑pilot; pre‑send reviewer for professional services.

Quotes:

Local AI means the model runs on hardware you control.

A smaller model in the right place can actually be very valuable.

Local first pass, cloud escalation, human approval — that’s how work’s going to get done.