Apple Silicon, DDR4 Duel for Local LLMs

The practical answer is that model quality and context usually matter more than peak speed, but CPU-only DDR4 builds can become painfully slow once you ask them to behave like real agents.

• –Apple Silicon unified memory behaves like shared VRAM, so 16GB to 24GB can still run surprisingly capable quantized models with usable responsiveness
• –A 64GB DDR4 mini PC gives you more room for larger models and longer context, but without a GPU or high-bandwidth memory the user experience can degrade fast
• –For coding and debugging, the sweet spot is usually the cheapest setup that can run a strong quantized model without constant memory pressure
• –The thread’s implicit warning is that “more RAM” does not automatically mean “better AI server” if the memory subsystem is the bottleneck
• –For a student budget, a balanced system with enough capacity for a good 7B to 27B-class model is often more useful than a tiny, fast model or a huge, sluggish one