Llama.cpp: A Game-Changer for Local Multimodal Chatbots Running a fully functional multimodal chatbot on your personal computer is now easier than ever, thanks to Llama.cpp. With just one command, you can set up a powerful chat interface that supports both text and images, all locally on your device. Best of all, this solution is entirely free and can run on almost any computer, including low-end machines like my own budget MiniPC, which I purchased for around $100 on Amazon. Starting from release b5331, Llama.cpp has added native support for multi-modality in the llama-server. This is a significant update because it means the chatbot can now handle visual data and text seamlessly. The ability to process and respond to images in addition to text makes this tool incredibly versatile and opens up new possibilities for interaction. Here are the four simple steps to get your local multimodal chatbot up and running: Install Llama.cpp: Begin by cloning the Llama.cpp repository from GitHub. This will give you access to the latest code and tools. Set Up the Server: Once Llama.cpp is installed, configure the llama-server with multi-modality support. This involves specifying the model you want to use, such as InternVL-2.5–1B, and ensuring the server is set to handle both text and image inputs. Run the Server Locally: Start the llama-server on your machine. Even if you have limited computing resources, Llama.cpp is optimized to run smoothly, making it an excellent choice for a wide range of devices. Interact with the Chatbot: Open a web interface or a terminal and begin chatting with your new multimodal chatbot. You can type text messages and upload images, and the chatbot will respond appropriately, providing a rich and engaging experience. To demonstrate how well this works, I decided to test it on my modest MiniPC immediately after the release. The results were impressive. The InternVL-2.5–1B model ran smoothly, handling both text and images with ease. This shows that Llama.cpp is not only powerful but also highly adaptable, capable of delivering high-performance results on low-cost hardware. For those new to Llama.cpp, let me assure you that it's user-friendly and well-documented. Whether you're a seasoned developer or just starting, setting up this chatbot is a straightforward process. The fact that it can run on such a diverse range of devices is particularly noteworthy, as it democratizes access to advanced AI capabilities. In conclusion, Llama.cpp offers a compelling solution for anyone interested in exploring generative AI with a multimodal chatbot. Its ease of setup, local operation, and support for various models make it a valuable tool for personal projects, experiments, and even educational purposes. Seeing is believing, and my own experience with a budget MiniPC is a testament to its effectiveness. Give it a try, and you might be surprised by what you can achieve.