NVIDIA DGX Spark vs. Mac Studio M5 Ultra: Which One Should You Actually Buy to Run AI Locally?

Two machines just became real purchase options for anyone who wants to run AI models on their own hardware instead of renting time on someone else's cloud servers: NVIDIA's DGX Spark and Apple's new Mac Studio with the M5 Ultra chip. Both get marketed with big numbers. Neither marketing page tells you which one actually fits what you're trying to do. This article works through the real differences - in plain terms, but without skipping the technical details that actually decide which machine is right for you. The Machines, Side by Side NVIDIA DGX Spark: $4,699. 128GB of memory, shared between the CPU and GPU. Memory bandwidth of about 273GB/s. It has been shipping and available to buy since late 2025. Mac Studio (M5 Ultra, 36-core CPU / 80-core GPU, 256GB): $10,799. 256GB of memory. Memory bandwidth of about 1.2TB/s (roughly 1,200GB/s). Ships September 22, 2026. A 512GB version of the same chip exists but won't be available until late October. Put simply: the Mac has twice the memory, more than four times the memory bandwidth, and costs more than twice as much. But "more numbers" doesn't mean "better for you" - these two machines are built for different jobs, and the numbers only make sense once you understand what each one is actually for. Two Different Kinds of Machines The Mac Studio is a complete workstation. You sit at it. Your files, your applications, and the AI model all live on the same box, and you interact with it directly like any computer on your desk. The DGX Spark is not meant to be sat at. It's designed to live somewhere else - a shelf, a closet, a server rack - and be reached over your network. A common setup is to keep using your regular laptop for everyday work, and connect to the Spark remotely (tools like Tailscale make this simple, creating a private network connection to it from anywhere) to send it AI requests the way you'd call an API. Your daily computer doesn't change at all; the Spark just becomes a private AI server you access when you need it. This distinction matters more than the spec sheet does. If you want one machine that does everything, including running AI models, the Mac is the obvious fit. If you already have a laptop you're happy with and just want a dedicated AI engine sitting quietly in the background, the Spark is the better shape for that job. Why Memory Bandwidth Is the Bottleneck That Actually Matters To understand why the bandwidth gap (273GB/s vs. 1.2TB/s) matters so much, it helps to know what an AI model is actually doing on this hardware, and what "memory bandwidth" means here. An AI language model is essentially a very large set of numbers (its "parameters" - think of them as everything the model learned during training) stored in memory. To generate a response, the machine has to repeatedly read those numbers out of memory and run calculations with them. Memory bandwidth is simply how fast the hardware can move that data from memory to the processor doing the math. A slower pipe means the processor spends more time waiting for data and less time actually computing - the same way a fast chef with a narrow doorway to the pantry still can't cook any faster than ingredients can be carried through it. Running an AI model on your own machine happens in two distinct phases, and they behave very differently: Prefill is when the machine reads and processes your entire prompt - everything you typed - before it starts responding. This step can be split across many parallel calculations at once, so it's mostly limited by raw processing power rather than memory bandwidth. Both machines handle this reasonably well. Decode is when the machine generates the actual response, one word (technically, one "token" - a chunk of text, sometimes a whole word, sometimes a fragment) at a time. Each new token has to be produced by reading through the model's parameters again, in sequence, one step at a time. This step cannot be parallelized the same way prefill can, which means it's almost entirely limited by memory bandwidth - how fast that data can be pulled from memory, over and over, for every single word of the response. This is exactly why the DGX Spark's low bandwidth (273GB/s) shows up as sluggish response generation: it can prepare for an answer quickly, but producing that answer word by word is bottlenecked by how much data it can move per second. The Mac Studio's 1.2TB/s bandwidth means it can sustain much faster token-by-token generation, which is the part of using an AI model you actually sit and wait for. Both Machines Still Crush a Normal PC on Raw Computing Power None of this should be read as "bandwidth is everything and raw compute doesn't matter" - it very much does, just not for the specific step described above. It's worth being clear about how much more computing power both of these machines have compared to an ordinary computer, because that gap is enormous and it shows up everywhere except that one narrow bottleneck. A typical PC without a dedicated AI-capable GPU is doing this kind of math on a handful of general-purpose CPU cores, built for everyday tasks like running apps and browsing the web, not for crunching millions of parallel calculations at once. The DGX Spark's Blackwell chip, by contrast, is rated for up to 1 PFLOP (1,000 trillion calculations per second) of AI compute at the precision level typically used for running these models, backed by a 20-core processor and dedicated tensor cores built specifically for this kind of math. The Mac Studio's M5 Ultra pairs a 36-core CPU with an 80-core GPU and a 32-core Neural Engine, and Apple states it delivers up to 4.5 times the AI compute of the previous M3 Ultra generation. Both numbers are on a completely different scale from what a normal desktop or laptop can do. That raw compute advantage is exactly why both machines can handle tasks a normal PC simply can't do at a usable speed at all: processing your prompt (the prefill step, which - as noted above - is parallel-friendly and genuinely compute-bound), running image or video generation, handling several AI requests from different people at once, or fine-tuning a model on your own data. In all of those cases, having far more cores and far higher raw throughput than a regular computer translates directly into faster, more capable performance - this is the everyday sense in which these machines are dramatically faster than a normal PC, and it's true for both of them. The nuance is specifically about one narrow, sequential step: generating a chat response one token at a time from a model already sitting in memory. That step doesn't get to spread its work across all those extra cores, because each token depends on the one before it - so it's gated by how fast data can be pulled from memory, not by how many cores are sitting idle waiting for that data to arrive. That's the one case where a chip with less raw compute but far more memory bandwidth (like the Mac Studio here) can end up feeling faster than a chip with more raw compute but less bandwidth. Everywhere else, more cores and higher compute really do mean better performance - over a normal PC, and often over each other. The Software Problem Nobody Mentions: CUDA vs. MLX Raw hardware specs are only half the story. Software support is the other half, and it's where NVIDIA has a decades-long head start. CUDA is NVIDIA's software platform for running heavy computations on its GPUs, and it has been the default foundation for machine learning for over a decade. The overwhelming majority of AI tools, libraries, and frameworks (PyTorch chief among them) were built and optimized for CUDA first. If you're using an NVIDIA machine like the DGX Spark, you're plugging into an enormous, mature ecosystem that "just works" with almost anything you download. Apple Silicon, including the M5 Ultra, doesn't run CUDA at all. Instead, Apple has built its own framework called MLX, designed specifically to take advantage of Apple's unified memory architecture. MLX is genuinely good, and it handles a large share of everyday AI tasks (particularly running, or "inferencing," existing models) perfectly well. But it's a smaller, newer ecosystem - plenty of cutting-edge research code and tools are released for CUDA first, or only for CUDA, and never get an MLX version at all. Practically, this means: if you want to run a well-established model for everyday use, the Mac's software situation is fine. If you want to experiment with the newest research code, fine-tune models yourself, or use tools that assume an NVIDIA GPU (which is most of them), the DGX Spark's CUDA compatibility gives it a real, practical advantage that the spec sheet alone doesn't show. How a Machine This Small Can Run a "Huge" Model Both of these machines are, by AI infrastructure standards, tiny. Yet both are being marketed as able to run models with hundreds of billions of parameters - sizes that used to require racks of datacenter GPUs. The reason this works is an architectural choice called Mixture of Experts (MoE), and it's worth understanding because it's the actual enabling technology behind both machines' big claims. A traditional ("dense") AI model activates every single one of its parameters for every word it processes. If the model has 200 billion parameters, generating one word means running calculations across all 200 billion of them - regardless of how simple or complex that particular word is to predict. A Mixture of Experts model doesn't work that way. Instead of one giant network, it's built from many smaller sub-networks (called "experts"), and a routing mechanism decides, word by word, which small subset of those experts actually needs to be used. A model might have 200 billion parameters in total, but only activate around 20 billion of them for any given word - the rest sit unused for that step. Real-world examples of this design include Mixtral and DeepSeek's models. This is exactly why a single consumer-scale machine can handle a "200-billion