Choosing the Right Local LLM for Your Mac: A Developer's Real-World Guide to Parameters, Quantization, and Model Architecture

I tested four local LLMs on my 36GB Apple Silicon Mac with the same Unity/C# prompt, and the results were not what the model names suggested. The fastest model was roughly 10x faster than the slowest. The "code" model refused to write the code. The best answer came from a distilled model that felt smarter in practice than a larger alternative. That is why choosing a local model is harder than sorting by parameter count. Architecture, quantization, active parameters, context window, and actual behavior under your prompt matter more than the headline number. Why Run LLMs Locally? I do not think local models replace Claude, GPT, or other frontier cloud systems. I use them as supplements, not substitutes. But they are already useful enough that every Mac developer should understand where they fit. The biggest benefit is cost. If I want to iterate on the same task ten times, local inference turns that into a zero-API-cost workflow. Then there is offline capability, IP protection, and freedo