Mac Studio vs DGX Spark: Which to Buy for Private AI in Your Office

Writing speed: the Mac is faster for one person

How fast a local model writes depends mostly on memory bandwidth, and the M5 Ultra has about four times the Spark's. Public tests line up with that:

• On the llama.cpp project's standard small-model test (Llama 2 7B, Q4_0), community results show about 179 tokens per second writing on an M5 Ultra, 120 on an M5 Max and 57 on a DGX Spark.
• On a bigger model people actually use, gpt-oss 120B, oMLX's published benchmark shows about 110 tokens per second writing on a 256GB M5 Ultra, while llama.cpp's own DGX Spark benchmarks show 58.7.
• In a Nov 10, 2026 head-to-head, Tom's Hardware found a 256GB M5 Ultra Mac Studio finished an agent benchmark (Qwen 3.8 27B on llama.cpp) in just under half the time of a single GB10 box, a Dell Pro Precision GB10 that is nearly identical to the DGX Spark.

These come from different software builds, so treat them as a rough guide, not a lab result. For reference, 20 tokens per second is already faster than most people read.

Reading long documents: closer than you'd think

Before a model answers, it has to read your prompt, and that step depends more on raw compute than on bandwidth. In the llama.cpp DGX Spark benchmarks, the Spark read a 2,048-token prompt for gpt-oss 120B at about 2,444 tokens per second; oMLX's M5 Ultra result for the same model was 1,049 tokens per second on a 1K prompt and 2,829 on a 4K prompt. If your main job is feeding a model long spec sections, the gap between the two is much smaller than the writing-speed gap.

For models too big for one Spark (Tom's Hardware used DeepSeek V4.1 Flash and GLM 5.3 Flash), a pair of linked Sparks read prompts much faster than the Mac running llama.cpp. With a TensorFold serving recipe for GLM 5.3 Flash, the Mac got much closer on prompts and wrote about three times faster than it did on llama.cpp. Software support matters as much as the hardware at this size.

What fits: memory decides which models you can run

• DGX Spark (128GB): NVIDIA rates one unit for models up to 200 billion parameters. It fits gpt-oss 120B and 70B-class models, but dense 70B models write slowly on it (LMSYS measured about 2.7 tokens per second for one user on Llama 3.1 70B).
• Mac Studio M5 Max 128GB: about 96GB usable by default; gpt-oss 120B fits.
• Mac Studio M5 Ultra 256GB / 512GB: about 192GB / 384GB usable; these reach models like Qwen3 235B-A22B that won't fit on one Spark.

Our model and hardware fit guide has the full fits-on tables.

Sharing it with the office: the Spark's strong suit

If several estimators, or an AI agent running all day, will hit the same box, the Spark's software is built for that. vLLM and SGLang serve many requests at once: LMSYS measured a small model going from about 20 tokens per second for one user to 368 combined across 32 users on a Spark. At idle and under light load it draws little power (ServeTheHome measured 40–45W idle and 60–90W running a model). But on a fixed agent workload, Tom's Hardware found an M5 Ultra Mac Studio used about 16% less total energy than a single GB10 box, because it finished in under half the time. The trade-off is that the Spark is a Linux server, so someone in the office has to be comfortable with a command line or NVIDIA's setup playbooks.

Price per gigabyte of model memory

Using the prices above (our arithmetic, rounded):

Spark rows use NVIDIA's list price; partner prices are higher today (Tom's Hardware put a 4TB Dell Pro Precision GB10 at about $10K on Nov 10, 2026). Per gigabyte, the bigger Macs are not more expensive than a Spark. Remember that a Mac only gives models about 75% of its memory by default.

Availability right now

As of Nov 10, 2026, Tom's Hardware reports that a high-memory Mac Studio takes 13 to 15 weeks to ship, with lead times stretching into February 2027. On the NVIDIA side, the 128GB DGX Spark has often shown as out of stock on NVIDIA's marketplace, Lenovo set its ThinkStation PGX (GB10) systems to "coming soon" during Tom's Hardware's testing, and a US reseller listing we checked for a 64GB Dell Pro Max GB10 still said "Coming Soon" with no price. Check stock and delivery dates before you plan around either machine.

Which one should you buy?

Pick a Mac Studio if:

• One or two people will use it, and you want the fastest answers.
• You want it to double as a regular office computer.
• You want room for the biggest open models on one box (256GB or 512GB).
• You'd rather use point-and-click tools like LM Studio or oMLX.

Pick a DGX Spark if:

• It will sit in a closet as a shared server for the whole office or an always-on agent.
• Quiet running and low idle power matter more than single-user speed.
• The software you want needs NVIDIA CUDA (vLLM, SGLang, TensorRT-LLM).
• You're comfortable with Linux, or willing to follow NVIDIA's playbooks.
• You want to run the very largest open models with the widest software support, and you're willing to buy two units and link them (Tom's Hardware estimates $15K to $20K+ for a pair with 4TB drives as of Nov 10, 2026).

Not sure yet? Don't buy either. Run a small model on the computer you already have (see chat with your specs locally) and test it on questions you already know the answers to. Buy hardware once you know which model size does your job well. Setup guides: local AI on a Mac Studio with MLX and DGX Spark setup. Weighing outside help instead? See AI consultant vs DIY.

Our news coverage: 512GB M5 Ultra Mac Studio pricing · 64GB DGX Spark · eight-unit Spark clusters · Muse Spark open weights on a Mac Studio