Mac vs Custom RTX 5090: Which Should You Actually Buy for ML Work?

If you're doing serious machine learning—especially fine-tuning and inference on large pretrained models—this decision matters way more than most people think. You're looking at 70% of your work being fine-tuning and pipeline building, 30% training from scratch, mostly with image/video data and occasional LLMs. Here's the thing: everyone assumes you need a beefy NVIDIA GPU for ML, but Mac's unified memory architecture is genuinely competitive for inference-heavy workflows. The catch? VRAM is your bottleneck, and while Apple's MLX is closing the gap with CUDA, the ecosystem around NVIDIA is still way ahead. A custom 5090 gives you raw power and proven compatibility; a high-end Mac gives you efficiency and surprisingly good performance on pretrained models. The real question isn't which is "better"—it's which workflow matters more to you. Read the full breakdown to see the actual benchmarks and cost-per-TFLOP.