opensource models

Practical guide to run ChatGPT-style models locally on Linux for privacy, speed, control, and predictable cost. Presents two paths: Ollama for the fastest OpenAI-compatible server (usable from Python/curl) and llama-cpp-python for a pure-Python runner that can also serve an API. Includes apt/dnf/zypper installs, CPU/GPU notes, Python/Bash examples, systemd unit, model picks, and performance tuning to automate real work off-cloud.

The article guides Linux users to run open-source LLMs locally for privacy, speed, and cost control, offering three paths: easiest with Ollama (CLI/API), more control by building llama.cpp (CPU/GPU, GGUF), and a web UI via Open WebUI + Ollama. It covers prerequisites, model/quantization choices, tuning (threads, GPU offload, context), bash automations, resource monitoring, and updates—ending with quick demos and action steps.