Local AI Testing on M1 Mac: Expectations vs. Reality

Local AI Experience and Hardware Realities

As the use of local artificial intelligence models gains popularity in the tech world, the hardware requirements of these systems can create unexpected challenges for users. A test conducted by an experienced AI reporter examined the performance of a language model running on the open-source Ollama platform on an M1 MacBook Pro.

Local Model Trial with Ollama

The model selected for the test, glm-4.7-flash, was described as a 30-billion parameter language model developed by the Chinese AI company Z.ai. It was noted that even this model, considered 'small' by today's standards, occupies 19 GB of disk space. Although downloading and installing the model was relatively easy, its performance did not meet expectations.

Performance Results and Hardware Limitations

During the test, it was observed that generating a response to a simple question like "What type of large language model are you?" took the model approximately one hour and sixteen minutes. A noticeable slowdown in the system's overall performance was recorded throughout this period. The reporter conducting the experiment emphasized that a three-year-old MacBook Pro with 16 GB of RAM might be insufficient for such AI workloads.

While running local AI models offers advantages such as data privacy, cost control, and customization opportunities, hardware requirements stand out as one of the significant barriers to this transition. Especially in today's environment where open-source AI assistants like OpenClaw gain access to operating systems, hardware optimization for local AI systems is becoming even more critical.

Advantages and Future of Local AI

Experts note that using local AI has advantages such as preventing sensitive data from being processed in the cloud, avoiding the continuously increasing costs of online AI services, and providing greater control over the model. However, test results show that most current personal computers lack sufficient hardware resources to run large language models efficiently.

Technology analysts predict that as the local AI ecosystem matures and hardware manufacturers develop specific optimizations for these workloads, local AI usage will become more accessible. During this process, users are advised to carefully evaluate their hardware specifications and shape their expectations accordingly.