Introducing different options for running Open Source LLMs, motivations and reasons on why doing it in-house instead of using closed source third-party APIs, in which escenarios make sense, and presenting two different examples: how to execute LLM inference from a web application, and from a Jupyter Notebook. In both cases leveraging any GPU enabled machine you have access, through the usage of oshepherd (https://github.com/mnemonica-ai/oshepherd), Ollama, and a Redis server.