Running local models on an M4 with 24GB memory (jola.dev)

A tech enthusiast has successfully set up a local AI model on a MacBook Pro with 24GB of memory, allowing for significant functionality without relying on internet connectivity. This achievement is noteworthy for the AI/ML community as it reduces dependence on large tech companies and fosters more sustainable AI usage. The model, Qwen 3.5-9B, while not matching the capabilities of state-of-the-art models, demonstrates viable performance for tasks like coding assistance and research, providing a unique blend of convenience and engagement. Setting up the local model involves choosing from various frameworks like Ollama, llama.cpp, or LM Studio, each with its complexities. The configuration process requires careful adjustment of parameters such as temperature and context length to optimize performance. The author reports achieving a capability of around 40 tokens per second while enabling advanced features like tool use, though the model sometimes struggles with complex directives. This local approach opens doors to a more engaging and hands-on experience with AI technologies, encouraging users to actively participate in the problem-solving process while minimizing ongoing subscription costs and environmental impact.