Mac RAG debate weighs native, llama.cpp, containers

llama.cpp is less a Mac speed hack than a distribution strategy. If your stack is already Python-first and the models fit MPS, native is the simpler path; if you want quantized GGUF models and one runtime across Mac and Linux, llama.cpp earns its keep.

• –PyTorch's MPS backend already exposes Apple's Metal GPU path to Python, so native Mac inference is a real option.
• –llama.cpp's own docs say Metal is enabled by default on macOS, and its build/Docker story spans Linux and Mac, so portability is the big differentiator.
• –The real llama.cpp advantage is the C/C++ + GGUF + quantization stack, which usually means lower memory and a smaller deployment surface.
• –A CPU-only container on Mac is mostly a reproducibility move; if Metal is the target, native is usually cleaner.
• –For a small reranker like Qwen3 0.6B, GPU use is often about latency headroom and batch throughput, not raw necessity.