Honestly, this is not a fair fight, they target different valleys of the local AI landscape. The DGX Spark is a mini data center for the desk, built for CUDA-native training loops where every FP16 flop matters. Its thermal design and power envelope mean you can push a 70B model's training to completion in hours, not days. However, for the same price, the Mac Studio M3 Ultra gives you more memory headroom for running inference on the largest quantized models (e.g., 120B) silently, without worrying about throttle or electricity bills. If you never train models, the Mac Studio is the obvious call; if you do, the Spark is the only serious choice.
If you're a pure 'builder running local AI' who lives in Jupyter notebooks and likes pushing code to cluster nodes, skip both and look at used A100 80GB servers. But between these two, the decision is clear: trainers pick Spark, inference-first pick Mac Studio. The real losers are those expecting a single box to do both equally well, neither device is a generalist, and that's fine. Just know that the DGX Spark will age slower due to software ecosystem, while the Mac Studio will age slower due to memory capacity.