Mac mini M4 Pro weighs local LLM stacks

This is a classic Apple-silicon local-inference question: the hardware is strong enough to run a meaningful private AI stack, but the “best” backend depends on whether you value convenience, throughput, or Apple-native optimization.

• –64GB unified memory puts the Mac mini in the sweet spot for local assistants, where larger quantized models become practical without immediately jumping to a GPU server.
• –LM Studio is the easiest fit for agentic workflows because it offers local server mode, OpenAI-compatible endpoints, and structured output tooling for apps and automation.
• –Ollama is the simplest operationally, and its March 30, 2026 MLX preview suggests Apple-silicon performance is becoming a bigger part of its pitch.
• –MLX is the most Apple-native route and likely the best bet for squeezing performance out of M4 Pro, but it usually means more hands-on setup and less turnkey ergonomics.
• –vLLM is the least natural default here: its official docs are Linux-first, so it is better suited to server GPUs than a Mac mini backend.