Can I run AI locally?
Yes—sometimes. Running AI locally is trending because open-weight models and better tooling make it possible on ordinary machines, but whether it works depends on memory, quantization, and how much speed you are willing to trade for privacy and control.
What is it?
Running AI locally means the model weights and inference stay on your own device instead of a remote cloud service. Tools such as llama.cpp, Ollama, and MLX have made it much easier to test open models on laptops, desktops, and even some phones.
Why is it trending?
The idea is trending now because open-weight models keep improving while consumer hardware and software stacks have gotten much better at squeezing them into limited RAM or VRAM. That has turned a vague dream—"AI on my machine"—into a practical question about model size, memory bandwidth, quantization, privacy, and cost.
Key Things to Know
Memory matters more than people expect
The first question is usually whether the model can fit in RAM or VRAM, not whether your device can simply install the app.
Quantization changes the trade-off
Lower-bit formats shrink memory use so larger models can run locally, but they also involve trade-offs in quality, speed, or both.
Local AI is a control-vs-convenience choice
People run models locally for privacy, offline access, customization, and lower marginal cost—but cloud systems still win on scale and simplicity.
Learn it in 5 questions
10 questions to test your understanding