Key LLM Concepts You Need to Run AI Models Locally on Your Own Machine

Running AI models locally requires understanding the distinct roles of the model file, the server (such as Ollama or LM Studio), the local API, and client applications that connect to it. Large language models work by predicting the next chunk of text using learned numerical weights, and running them on personal hardware is called inference. Hardware resources — particularly GPU VRAM, system RAM, and CPU — determine whether a model runs efficiently, with model weights needing to fit into VRAM for optimal speed. Model parameter counts, measured in billions, indicate capability and hardware demand, ranging from lightweight 1–3B models suitable for any laptop to 70B-plus models requiring workstation-grade hardware. For conversational use and automated workflows, instruct-tuned model variants are recommended over base models, as they are specifically trained to follow instructions.