1. AIGC is a paradigm shift from goal-oriented problem solving to free-form interactive engineering. It is time to expand our imagination to products that can talk and draw with the customers, on top of being able to completing its own tasks with these interactions. Your fridge can help to order groceries when asked, but can also answer generic questions like ChatGPT does. No reason to limit the AI to do what its shell product is designed to do. For manufacturers of these products, it means better customer stickiness. 2. The entire AIGC economy is in its infancy because right now the paying customers are the tech-savvy people who can afford a few tens of bucks in subscription fees every month. To make it really ubiquitous in every product and every place, the AI model serving cost must be reduced by multiples of thousands of times. When that is achieved, products like GitHub copilots might just be free like Bing search. At that time, every product that is capable of accessing the Inter
This blog post is a step-by-step guide for running Llama-2 7B model using llama.cpp, with NVIDIA CUDA and Ubuntu 22.04. llama.cpp is an C/C++ library for the inference of Llama/Llama-2 models. It has grown insanely popular along with the booming of large language model applications. Throughout this guide, we assume the user home directory (usually /home/username) is the working directory. Install NVIDIA CUDA To start, let's install NVIDIA CUDA on Ubuntu 22.04. The guide presented here is the same as the CUDA Toolkit download page provided by NVIDIA, but I deviate a little bit by installing CUDA 11.8 instead of the latest version. At the time of writing, PyTorch 2.0 stable is released for CUDA 11.8 and I find it convenient to keep my deployed CUDA version in sync with that. $ wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb $ sudo dpkg -i cuda-keyring_1.1-1_all.deb $ sudo apt update $ sudo apt install cuda-11-8 After