The model
The trained neural network. Models vary in size, capability, licence, context length and resource needs. “Bigger” is not automatically “better for your task”.
Level 6
Run AI on your own hardware
A local model gives you more control over where inference happens and how it is integrated. In return, model choice, GPU memory, software updates, access control, performance and backups become your responsibility.
Four pieces to understand
The runtime
Software that loads and serves the model. A tool such as Ollama can run supported models locally and expose an API for local applications.
The hardware
GPU VRAM strongly influences what model sizes and context workloads are practical. CPU and system RAM also matter, but local AI planning should begin with the actual workload rather than buying a card by model number alone.
The application
A chat UI, coding assistant, document tool or your own software calls the runtime. Local inference does not automatically make the surrounding application secure.
When local AI makes sense
Privacy/control
You need inference on hardware you control and have assessed where the data, logs and application state live.
Repeat usage
A predictable local workload may justify hardware and operational cost compared with repeated hosted inference.
Experimentation
You want direct control over models, quantization, system prompts, local APIs or integrations without depending entirely on a hosted service.
“Local” does not mean “magically private”. Check the whole path: user interface, runtime binding address, plugins, telemetry, logs, file permissions, backups and any external services the application calls.
A sensible first local experiment
- Choose a local runtime you understand and install it from its official source.
- Start with a model that clearly fits your available memory rather than the largest one you can find.
- Use it interactively first; measure quality and speed on tasks you actually care about.
- Only then connect it to files or applications, using the same least-privilege principles as Level 5.
- Keep a hosted AI available as a comparison. Local and cloud models are tools, not religions.
Do not buy hardware before defining the workload. Coding assistance, occasional chat, long-document analysis and high-concurrency serving can imply very different model and VRAM requirements.