Running an AI Assistant on Your Own Computer Instead of the Cloud
A local AI assistant needs a runtime, downloaded model files, enough memory to load the model and either a chat interface or local API; once configured, many setups can work without internet access.
To run an AI assistant on your own computer, you need four basic pieces:
- a local AI runtime;
- a model file;
- enough RAM or VRAM to load it;
- a way to talk to it.
The exact software can vary, but that architecture stays fairly consistent.
Choose a runtime
Common options include:
- LM Studio — desktop interface, model management, local chat and local API;
- Ollama — local service, CLI and API;
- llama.cpp — command-line inference and local server tooling.
None is automatically best for every user.
Choose based on the operating system, hardware and interface you want.
Download a model
The runtime is not the model.
You still need model weights.
Model files vary dramatically in size.
A smaller quantized model may fit comfortably on an ordinary computer, while a larger model can require much more memory and storage.
Download one that actually fits your hardware rather than starting with the largest model name you can find.
Load the model into memory
During inference, the runtime must load enough model data into system RAM, GPU VRAM or both.
If the model is too large, you may see:
- very slow loading;
- out-of-memory errors;
- heavy swapping;
- poor response speed.
Some runtimes can split work between CPU and GPU or use quantized models to reduce memory demand.
See What Kind of Computer Do You Need to Run AI Locally?.
Use a chat interface or local API
LM Studio includes a chat interface.
Ollama can be used from the command line or through applications that call its local API.
llama.cpp can expose a local server with OpenAI-compatible-style endpoints.
A local API is useful when you want:
- a script to call the model;
- a desktop application to use local inference;
- an automation tool to generate or classify text;
- several local applications to share the same runtime.
Test whether it is really local
Once the model is downloaded, disconnect the network and test again.
A genuinely local chat workflow should continue working.
LM Studio currently documents offline operation for downloaded models, local chat, document chat and its local server.
If the feature stops working offline, identify which part depends on the network.
Local models do not have live web knowledge by default
A downloaded model contains what was learned during training plus the context you provide at runtime.
It does not automatically know today's news, weather or website changes.
Current information requires some additional data source, such as:
- web search;
- local documents;
- databases;
- APIs.
Those additions may create network traffic and privacy implications.
Update the runtime and models separately
The runtime software can be updated.
Models can also be replaced or upgraded independently.
That separation is useful because you can keep a stable application while testing different models, or keep a known model while updating the runtime.
For the broader concept and tradeoffs, see What Is Local AI, and Why Should Kirksville Computer Users Care?.
A local assistant is not one product. It is a small stack: runtime, model, memory, interface and optional tools.