Iternal Technologies

How to Run an LLM Locally, Summarized?

To run an LLM locally, install a tool (Ollama, LM Studio, or llama.cpp), download a quantized open model that fits your RAM or GPU, then run it — all entirely offline and free. On a modern laptop with 16 GB of memory you can run a 7B–13B model in minutes with no internet, no API key, and no data ever leaving your device. The whole setup takes one download and one command.

How Do You Run an LLM Locally?

You run an LLM locally by installing a runtime (Ollama, LM Studio, or llama.cpp), downloading a quantized open-weight model that fits your memory, and then running that model directly on your CPU or GPU — with no cloud, no API key, and no internet after the initial download. The entire workflow is free and open source, and a capable laptop is enough to start.

What Do You Need to Run an LLM Locally? (Hardware Checklist)?

You need three things: a local LLM tool, a quantized model file, and enough RAM or VRAM to hold it. Memory is the single most important constraint. A practical rule of thumb is that a 4-bit quantized model uses roughly 0.6–0.7 GB of memory per billion parameters, so a 7B model fits in about 5–6 GB and a 13B in about 9–10 GB, with a few gigabytes of headroom for the operating system and the context window.

Which GGUF Quantization Should You Download?

Download the Q4_K_M build of the largest model your memory can hold. It averages about 4.8 bits per weight, cuts a 16-bit model to roughly a quarter of its size, and costs a quality drop most people never notice. Step up to Q5_K_M or Q6_K only when you have memory to spare, and drop below 4-bit only to fit a class of model that otherwise will not run.