Local AI guide · 7 min read
What can local AI do?
Local AI means the model runs on your own computer instead of sending every prompt to a hosted model. It is not always the smartest or fastest option, but it is very good for private, everyday workflows.
Quick answer
Local AI is useful for private chat, task planning, notes, document cleanup, coding help, offline work, and voice commands. The right model depends on your RAM, GPU, VRAM, and how fast you expect responses to feel.
Check your computerPrivate chat
Ask questions, draft text, rewrite notes, brainstorm names, or get a second pass on something without sending every prompt to a hosted model.
Notes and search
Summarize notes, find related ideas, clean up rough captures, and turn scattered thoughts into a usable list.
Local voice
Use a smaller speech-to-text model and a local chat model for private dictation, command capture, or simple voice replies.
Coding help
Explain snippets, draft small functions, review errors, and work through local files when your hardware has enough memory.
Task planning
Turn tasks, deadlines, habits, and energy level into a realistic plan for the next hour or day.
Offline workflows
Run useful AI workflows without relying on a live internet connection, which is useful for travel, flaky Wi-Fi, and private work.
Why run AI locally?
The main reason is control. If your workflow is full of private tasks, unfinished notes, customer context, school work, personal reminders, or half-written ideas, local AI lets you use an assistant without making cloud upload the default.
Local AI also makes small repetitive actions feel cheap. You can ask for a summary, clean up a note, sort a task list, or draft a reply without thinking about API usage or whether the request is worth sending somewhere else.
That is the direction Taby is built around: a desktop assistant that works with your own tasks, focus sessions, habits, and notes while keeping the important data on your computer.
What hardware do you need?
For a practical local assistant, start by thinking in model tiers. Tiny 1B to 3B models are light and fast. 7B or 8B models are a common daily-driver tier. 14B models are stronger but need more memory. Larger models can be useful, but they start feeling like workstation territory.
On Windows and Linux, GPU VRAM is often the limiter. On Apple Silicon Macs, unified memory is the limiter. CPU-only local AI is possible, but it usually feels slower, especially if you want voice or coding help.
Where local AI is weaker
- Very large models still need serious memory and GPU power.
- Local models do not automatically know fresh web facts unless you connect them to search or documents.
- Long context windows can use much more memory than short chats.
- Voice workflows need room for speech-to-text, the chat model, and text-to-speech.