I tried local AI on my M1 Mac, and the experience was brutal - here's why (www.zdnet.com)

A recent hands-on experience with the open-source AI tool Ollama highlighted the challenges of running large language models (LLMs) locally on a 2021 MacBook Pro. While Ollama simplifies downloading models and integrates well with tools like LangChain and Codex, the endeavor showcased significant technical limitations. The author struggled with sluggish performance and long response times, even when using a relatively "small" LLM, the 30-billion-parameter glm-4.7-flash. This experience underlines the growing hardware demands of AI, as even basic use requires a machine with at least 32GB of RAM, far exceeding the author’s current setup of 16GB. The implications for the AI/ML community are substantial. As more individuals and organizations consider running LLMs locally for both privacy and productivity reasons, the need for robust hardware will become increasingly critical. This encourages developers and users to rethink their infrastructure investments to support these powerful AI tools. The report serves as a wake-up call, indicating that while local AI operations can enhance control and reduce costs associated with cloud services, inadequate hardware can hinder usability and effectiveness. The burgeoning field of AI continues to demand escalating performance from end-user devices, pushing the boundaries of what is feasible on current technology.