Run useful AI on compute you already own.
Self-hosted AI gives small businesses a way to control private data, reduce repeat API costs, and turn spare machines into narrow AI workers.
Quick answer: A self-hosted model can make sense when the work is repeatable, privacy matters, or you already own usable hardware. A 16GB computer can support compact models for focused jobs, while GPU workstations can handle larger agents and heavier document workflows.
What self-hosted AI is actually good for.
Private docs
Search SOPs, contracts, policies, notes, and proposals without sending every query to a public API.
Repeat tasks
Run the same summarization, classification, routing, or drafting job many times at predictable cost.
Internal agents
Build a quiet worker for staff, not a customer-facing system that needs enterprise uptime.
Edge workflows
Run small models near the data source when internet access, latency, or control matters.
Start with the machine, then choose the model.
The selector below estimates whether a compact model can run on the compute you already own. It is a planning tool for scoping the first workflow.
1. What should AI handle first?
2. Where should it run?
3. Daily volume
4. Available compute
Llama 3.1 8B
Local support bots, RAG, private document work
Use MLX or Ollama and close memory-heavy apps before running long context windows.
This is a planning estimate, not a hardware guarantee. Final setup depends on documents, tools, uptime needs, and security rules.
Have an old computer sitting around?
Send the specs and the workflow. We will tell you if it is useful, what model class fits, and when cloud makes more sense.
Review my setup