Mac Studio M5 Ultra vs NVIDIA DGX Spark: The $5,500 Local AI Bet

A developer compared Apple's Mac Studio M5 Ultra and NVIDIA's DGX Spark as local AI machines, finding the Mac Studio offers 4.4x the memory bandwidth for faster LLM inference, while the DGX Spark provides CUDA software support and fine-tuning capabilities. The analysis highlights trade-offs in memory capacity, price, and power draw for running large models on-device.

Two weeks ago I priced out what it would take to run a 200B-parameter model in my apartment in Dhaka without cloud tokens. The used-GPU route kept dead-ending: a card with enough VRAM either doesn't exist at consumer prices or melts my budget on electricity before it melts anything else. So when Apple announced the new Mac Studio with M5 Ultra yesterday, and NVIDIA's DGX Spark has been sitting on the same shelf since earlier this year, the comparison wrote itself: two boxes, roughly $5,500 give or take, both pitched as the desk-side local AI machine. They could not be more different machines. Full disclosure up front: I have used neither. What follows is a spec-and-benchmark analysis from Apple's newsroom, NVIDIA's datasheet, and independent DGX Spark reviews. I run my own AI agent infrastructure and I write Java and Spring Boot for a living, so the buying lens here is a working backend developer's, not a researcher's. Strip away every marketing slide and two numbers decide which local AI box you should buy: how much memory it has, and how fast that memory moves. Memory capacity decides whether a model fits. Memory bandwidth decides how fast you can read weights, and LLM inference is almost entirely a memory-bandwidth problem once the model is loaded. That's 1.2TB/s versus 273GB/s. Apple's machine has roughly 4.4x the memory bandwidth. In the decode phase of LLM inference, where the model generates one token at a time and every token requires a full pass over the weights, bandwidth is close to the whole story. A bandwidth advantage of that size translates almost directly into tokens per second on big models. But capacity is where it gets interesting. 128GB fits a 200B-parameter model at 4-bit quantization, which is exactly what NVIDIA advertises: "support for up to 200 billion parameter models" on DGX Spark. 512GB fits vastly more, or fits big models at higher precision with long context. Apple's pitch is that you can run enormous LLMs entirely on device. Both claims are true. They're aiming at slightly different buyers. Raw decode throughput on big models: Mac Studio. The bandwidth math above does the heavy lifting here, and it matches community benchmarks: M3 Ultra owners report strong sustained token generation on 70B-class models, and M5 Ultra at 1.2TB/s raises that ceiling by 50 percent over M3 Ultra's 800GB/s. Everything CUDA: DGX Spark. If your local work involves anything beyond llama.cpp and MLX, the software story flips hard. DGX Spark runs the full NVIDIA AI stack: CUDA, CUDA-X, TensorRT-LLM, NIM microservices, and NVIDIA's own fine-tuning toolchain. The Java angle matters to me here: my Spring Boot services talk to inference servers, not raw GPU kernels, but every serious inference server vLLM, TensorRT-LLM was born on CUDA. Apple's MLX ecosystem is improving fast but it is still Apple-only, smaller, and slower to receive day-one support for new open models. Fine-tuning: DGX Spark, clearly. NVIDIA's own published numbers show a Llama 3.3 70B QLoRA fine-tune hitting a peak of 5,079 tokens per second of training throughput on DGX Spark. That is real fine-tuning work, not a demo. Apple has Core ML and MLX training paths, but nothing in the same league for the tuning-and-prototyping workflow NVIDIA has built around this box. Price per usable gigabyte: DGX Spark, today. $4,699 for a guaranteed 128GB, shipping now. The M5 Ultra at 96GB costs $5,499 and the 256GB step is +$4,000, with 512GB unpriced and late-October. If you need certainty this quarter, NVIDIA wins the purchase-order argument. Power draw: DGX Spark. Roughly 240W maximum versus the Mac Studio's far lower idle-plus-load profile, right? Wrong, actually. The original DGX Spark launched to complaints that it capped at 100W and thermally throttled under sustained load, with John Carmack publicly calling out the gap between the petaflop marketing and shipping performance. The Mac Studio draws more absolute watts but sustains them. Both are desk-side boxes that run off a standard wall outlet, which is the actual point: neither needs a circuit upgrade. NVIDIA quotes "up to 1 petaFLOP" of FP4 AI compute for DGX Sparse. FLOPs are a compute ceiling. LLM inference on big dense models is bandwidth-bound, and independent benchmarks show the real story. First DGX Spark LLM benchmarks compiled by Hardware Corner measured GPT-OSS 120B at roughly 1,723 tokens per second of prompt processing but only 38.6 tokens per second of token generation in MXFP4. A Level1Techs review measured Llama 3.3 70B at 5.4 tokens per second end-to-end in NVFP4 on TensorRT-LLM. Think about that for a second. The "petaflop on your desk" generates text at roughly the speed of a fast human reading, on models under 200B. Prefill prompt processing is fast; decode generation is bandwidth-starved. Apple's side has the same genre of caveat. The "4.3x faster AI performance" headline compares M5 Ultra to M3 Ultra, the two-generation-old chip in the old Studio, not to any NVIDIA hardware. Apple's own fine print: 4x faster LLM prompt processing in LM Studio, 4.3x faster text-to-image generation. Prompt processing again. Decode numbers on the new machines don't exist yet because reviewers don't have units until September 22. But the physics is the physics: 1.2TB/s against 273GB/s means the M5 Ultra's decode throughput on large quantized models should land well above DGX Spark's, and M3 Ultra decode benchmarks at 819GB/s already typically beat DGX Spark on large-model generation. One more Apple number worth knowing: Thunderbolt 5 clustering. Apple says you can link multiple Mac Studios with RDMA over Thunderbolt 5 and get up to 3x faster distributed AI inference with a four-node cluster. NVIDIA's equivalent is ConnectX-7 networking to pair two Sparks for models up to 405B parameters. Both work; neither is as clean as the single-box story. Here is the decision matrix I'd actually use, as someone whose local LLM work is prototyping Spring AI agents, testing retrieval pipelines, and occasionally fine-tuning small open models: If I were speccing my next local-AI box from scratch, knowing what I know now: I'd stop thinking in FLOPs and start thinking in bandwidth-per-dollar and capacity-per-dollar. On bandwidth-per-dollar at the ~$5K tier, the M5 Ultra wins decisively. On capacity-per-dollar today, the Spark wins because Apple's memory pricing is brutal. And I'd wait six weeks: the first independent M5 Ultra decode benchmarks will land in late September, and that is the single number that validates or kills Apple's local AI pitch. Apple built the better inference box. NVIDIA built the better developer platform. The M5 Ultra's 1.2TB/s against the Spark's 273GB/s is not a rounding error, it is a generation gap, and it shows up exactly where it matters: generating tokens from big models. But specs don't run your fine-tuning job or your TensorRT pipeline, CUDA does, and 128GB that ships today beats 512GB that ships in October if you have work due this quarter. Both companies just told you, with two very different machines, that they believe the next five years of developer compute is desk-side, private, and token-unmetered. That part I'm confident agreeing with. I write about Java, Spring Boot, and AI every week. Subscribe, it's free. Have you run local models on either platform? What do the real numbers look like on your desk?