Verified against primary docs · July 2026
Private AI on Your Computer
Install one local model, prove where it runs, and complete a useful task—with exact commands, privacy checks, and honest limits.
One payment · 17-page PDF · Windows, macOS, and Linux · Code LAUNCH30 saves 30%
One model, one real task
Check the complete data path
Download and keep it
Start with the machine you own.
The guide replaces fragile model rankings with a repeatable fit test. Start small, inspect the real CPU/GPU split, increase context deliberately, and keep the smallest model that passes your task.
| Your constraint | Practical starting point | What to measure |
|---|---|---|
| No dedicated GPU | Small quantized text model | Latency, swapping, repeatability |
| Dedicated GPU | Leave VRAM headroom | Processor split in ollama ps
|
| Long documents | Raise context only when needed | Memory growth and answer quality |
A working decision system.
Use the checklists in order, then test your setup on real work instead of endlessly changing models.
- Ollama first runCurrent Windows, macOS, and Linux paths plus exact verification commands.
- Model-fit testMeasure CPU/GPU allocation, context cost, latency, and output against one task.
- Privacy boundarySeparate local models from cloud models, tools, synced folders, and exposed ports.
- Open WebUIAdd a browser interface only after the core runner passes its terminal test.
- Three working workflowsSource-grounded summary, private drafting, and structured extraction.
- Troubleshooting mapLogs, GPU fallback, memory pressure, container networking, and weak answers.
See the system before you buy.
Before you download.
Do I need a dedicated GPU?
No. CPU-only setups can handle smaller quantized models, although generation will be slower. The guide explains sensible starting points by available memory.
Is this software?
No. This is a practical PDF setup and decision guide. It points you through compatible local-model tools and workflows; it is not a hosted application.
Will every model run on every computer?
No. Model choice depends on memory, hardware, quantization, and context length. The guide is designed to prevent unrealistic downloads and poor configurations.
Does “local” automatically mean private or compliant?
No. The guide shows how to verify the whole path—model, runner, interface, tools, folders, and network binding. It does not replace your security, legal, or compliance review.
What if the file is missing or damaged?
Email schephenk198@gmail.com within 14 days. Technical delivery problems and product mismatches are reviewed within two business days.
Put private AI to work today.
Install one suitable model, finish the acceptance test, and verify local-only behavior before introducing sensitive work or optional tools.