The zero-overhead inference vision: MLX vs. llama.cpp on Apple Silicon

Explored whether we could bypass llama.cpp entirely and compile straight via mlx-swift for zero-overhead Metal inference. The CudaBuild plugin validation failure in mlx-swift 0.31.6 was the trigger — if the MLX dependency is fragile, is it worth the complexity? The answer turned out to be nuanced: MLX is the fastest engine when compiled in Release (after the debug scheme discovery), but llama.cpp is the most portable and has the widest model format support. Both stay. The vision isn't picking a winner — it's giving the user three backends and letting the benchmark numbers speak for themselves. That's the whole point of Edge AI Lab: not opinions about which runtime is best, but reproducible data on your actual hardware.