I tested local AI on my M1 Mac, expecting magic - and got a reality check instead (www.zdnet.com)

A recent hands-on experiment highlighted the challenges of running local AI models on consumer-grade hardware, specifically an M1 MacBook Pro with 16GB of RAM. Using the open-source tool Ollama, the experiment aimed to execute large language models (LLMs) like GLM-4.7-flash and gpt-oss:20b. While Ollama simplifies the process of downloading and interfacing with AI models, the actual performance revealed significant limitations. Even smaller models struggled to provide timely responses, leading to a frustrating experience and emphasizing the need for at least 32GB of RAM to effectively run these models. This testing underscores a broader trend in the AI/ML community: as language models grow more complex, they demand increasingly powerful hardware. The attractiveness of local LLMs lies in their potential to enhance job prospects, protect sensitive data, and reduce costs associated with cloud services. However, as demonstrated in the experiment, running these models effectively requires more robust technical infrastructure than what many consumers may currently possess. This reality check spotlights the growing divide between those with access to advanced computing resources and those relying on older technologies, highlighting the need for continuous investment in hardware to leverage the advancements in AI effectively.