This blog post provides a step-by-step guide on how to set up a local Large Language Model (LLM) on a CPU and create a ChatGPT-like graphical user interface (GUI) in just 15 minutes. The tutorial walks through the process of selecting a suitable Huggingface LLM, quantizing the model for improved memory and speed, building an Ollama image to wrap the model, and setting up a Docker container to run the GUI. The article emphasizes the benefits of running LLM locally, particularly for organizations concerned about data privacy. With the detailed instructions provided, anyone can easily set up their own local LLM with a chat UI.
| Signal | Change | 10y horizon | Driving force |
|---|---|---|---|
| Running LLMs locally on CPU | Technological | More organizations running performant LLMs on consumer laptops or CPU-based servers | Need for local computing and data privacy |
| Quicker setup of local LLMs with GUI | Technological | Faster and easier setup of local LLMs with user-friendly interfaces | Accessibility and user demand for local models |
| Increasing use of quantized models | Technological | More models quantized for reduced memory usage and faster inference on CPU | Efficiency and performance improvement |
| Integration of LLMs into API frameworks like Ollama | Technological | Seamless integration of LLMs into API frameworks for easier deployment | Streamlining model deployment and accessibility |
| Development of chat UIs for local LLM interaction | Technological | Improved and user-friendly chat UIs for local LLM interaction | Enhanced user experience and ease of interaction |