What Is Local AI, and Why Should Kirksville Computer Users Care?

Local AI means the model runs on your own computer rather than sending every prompt to a remote cloud model.

That changes where the computation happens and where your data may go.

It does not automatically make the system private, secure, offline or better.

The runtime and model are separate

A local AI setup usually has at least two pieces:

• a runtime that loads and executes models;
• a model file containing the trained weights.

Examples of local runtimes include:

• LM Studio;
• Ollama;
• llama.cpp.

The runtime is the software engine.

The model is the thing being loaded into memory and used to generate responses.

The model has to live somewhere

Cloud AI services store and run their models on remote infrastructure.

Local AI requires the model files to exist on your machine.

That can mean several gigabytes or far more depending on the model and quantization.

You usually need internet access to download the model initially, unless you transfer the files from another machine.

Local models can work offline

LM Studio currently documents that downloaded local models can run entirely offline.

Ollama and llama.cpp can also run local models and expose local APIs for other applications.

That makes local AI useful when:

• internet access is unreliable;
• you want a self-contained workstation;
• you want local tools to call an AI model without sending each request to a cloud provider.

The model's knowledge does not magically update when the internet is disconnected.

Hardware matters

A small local model may run acceptably on CPU and system RAM.

Larger models often benefit significantly from GPU acceleration and more memory.

Important resources include:

• system RAM;
• GPU VRAM;
• CPU speed;
• memory bandwidth;
• storage space;
• model size and quantization;
• context length.

See What Kind of Computer Do You Need to Run AI Locally? for the hardware side.

Local does not automatically mean private

A model may run locally while the application still uses:

• cloud search;
• cloud-model fallback;
• remote tools;
• remote MCP servers;
• analytics;
• synced folders.

Privacy depends on the entire data path.

If privacy matters, test which components actually leave the computer.

See How Local AI Keeps Private Documents on Your Own Hardware.

Local AI has real tradeoffs

Advantages can include:

• offline operation;
• local control;
• no per-message cloud dependency;
• private workflows when the whole stack stays local;
• easy integration with local APIs.

Costs can include:

• model downloads;
• storage;
• hardware requirements;
• configuration;
• slower performance on modest machines;
• smaller models with less capability than leading hosted systems.

Who should care?

Local AI is especially interesting for:

• technical users who want automation;
• people working with private local files;
• users with unreliable internet;
• developers building local tools;
• businesses experimenting with controlled internal AI workflows.

For casual questions, a cloud service may still be simpler.

Local AI is not automatically the "better" kind of AI.

It is a different deployment model that gives the computer owner more responsibility and more control.