Why I Run Local LLMs Instead of Cloud Chatbots

Local LLMs are my default for daily AI tooling. Instead of sending every prompt to a cloud chatbot, I run models on my own hardware — and the practical benefits are hard to argue with.

Three reasons I prefer local models

1. Privacy. Prompts, code, and documents never leave the machine. No third party sees what I’m working on.
2. Zero marginal cost. No per-token pricing. Experiment as much as I want.
3. Control. I choose the model, the quantization, the system prompt, and the tooling around it.

Where local models still lag

• Small models can’t match frontier models on complex reasoning — I keep a cloud model for hard one-off questions.
• Hardware matters: a GPU or a lot of RAM changes what you can run comfortably.

Practical setup notes

• Linux is the friendliest host for local inference tooling — most frameworks target it first.
• Quantized models (GGUF and similar) make 7B–14B parameter models practical on consumer hardware.
• A modest setup covers most daily use: summarization, drafting, and code assistance.

My interest in local LLMs started as a curiosity and became a default workflow — and it directly shaped projects like ejobtrack, where ML inference runs on-device by design.