Kituwa IT · Field guide
Local AI: what actually stays local?
Local AI means the model runs on hardware you control. It does not automatically mean every part of the workflow is… Practical checks and official references.
Local AI means the model runs on hardware you control. It does not automatically mean every part of the workflow is private.
This guide expands the Facebook post into a practical workflow for beginners, home-lab users, and small-business owners who want to understand the trade-offs before changing a working system.
What this guide covers
- Local AI: what actually stays local?
- The model is one part of the path.
- Local
- Cloud
- Inference — Your hardware — Provider's servers
- Data path — Check the whole app — Prompts leave your device
Why it matters
The goal is not to memorize a product name or copy a command blindly. It is to understand what the change affects, verify it against an official reference, test it on spare hardware or a virtual machine when possible, and keep a rollback path.
A practical walkthrough
Test the model on your task
The model weights and inference can stay on your machine. But the app may send prompts to a cloud API, fetch remote data, or log conversations. A browser front end may load assets from the internet. Check each component.
Check the assumptions
Local models can be useful for offline drafts, private notes, coding help, and repetitive tasks. They can also be slower or less capable than hosted models, depending on hardware and model size.
Use the supported update path
Decide what must stay local: the prompt, the document, the output, or all three. Then verify the whole path, not just the model. A local model may still download weights, fetch updates, or call a plugin; those are network paths too. A local model is a tool, not a privacy guarantee. Read the app's network behavior. A local model may still download weights, fetch updates, or call a plugin; those are network paths too.
Your turn
What would you want to keep on your own machine?
What to verify
- Use the official documentation for the exact version you run.
- Test on a spare device or virtual machine before changing a working system.
- Keep a backup and a rollback path.
- Record what changed and how you verified it.
Common mistakes
- Trusting a confident answer without a check.
- Skipping a repeatable test set or factual review.
- Sending private data to an endpoint you have not verified.
Quick checklist
- ☐ The model is one part of the path.
- ☐ Local
- ☐ Cloud
- ☐ Inference — Your hardware — Provider's servers
- ☐ Data path — Check the whole app — Prompts leave your device
- ☐ Capability — Hardware-limited — Often larger models
FAQ
What is the first step?
Local AI means the model runs on hardware you control.
What should I avoid?
Trusting a confident answer without a check.
Where can I verify the current details?
Use the official references below; versions and interfaces change.
References
Related guides
Need help?
If you want a second opinion on the setup or the rollback plan, Kituwa can review it with you. Start with the checklist above; it makes the conversation more useful.