DGX Spark vs Mac Studio
We rent DGX Sparks, so treat us as biased, and then read the part where we tell you the Mac Studio is the faster machine for most people's daily local-LLM use. These two boxes look like competitors on a spec sheet and are genuinely different tools once they're on your desk.
The verdict, up front
Buy the Mac Studio if… you mostly chat with models interactively, you want the highest tokens/second on a model that fits in memory, you need one machine that is also your workstation, or you want more than 128 GB of memory in a single box. Apple's August 25, 2026 refresh makes this easier to recommend than it was a week ago: the new M5 Ultra Mac Studio does 1.2 TB/s of memory bandwidth (Apple's own figure, ~50% up on the M3 Ultra's 819 GB/s) and configures from 96 GB to 512 GB again, so the high-memory option is back on the new-order list instead of the used market. That is roughly 4.4× the Spark's 273 GB/s, and single-stream generation speed tracks bandwidth almost linearly.
Choose the Spark if… your work is CUDA-shaped, fine-tuning, TensorRT-LLM, vLLM, NeMo, Triton, CUDA kernels, or your prompts are long. The Spark's ~1 PFLOP FP4 Blackwell GPU chews through prefill several times faster than the Mac, and anything you build on it runs unchanged on a DGX/H100/GB200 later. That portability is the whole argument.
Side-by-side specs
| NVIDIA DGX Spark | Mac Studio (M5 Ultra, Aug 2026) | |
|---|---|---|
| Chip | GB10 Grace Blackwell (20-core Arm: 10× Cortex-X925 + 10× A725) | M5 Ultra, up to 36-core CPU (12 super + 24 performance) / 80-core GPU with Neural Accelerators / 32-core Neural Engine; quad-die, UltraFusion at 4.4 TB/s |
| Unified memory | 128 GB LPDDR5X (fixed) | 96 GB – 512 GB (512 GB ships late October 2026) |
| Memory bandwidth | 273 GB/s | 1.2 TB/s (M5 Max: 614 GB/s) |
| Peak AI compute | ~1 PFLOP FP4 (~100 TFLOPS FP16) | Apple does not publish a FLOPS figure; Apple claims up to 4.3× the AI performance of the previous-generation Mac Studio |
| Software stack | Full CUDA: PyTorch, vLLM, TensorRT-LLM, NeMo, Triton | MLX, llama.cpp, Ollama, Metal; no CUDA |
| Cluster interconnect | 2× QSFP ConnectX-7 (200 GbE), two units link natively | Thunderbolt 5 / 10 GbE |
| Power draw | ~240 W from a wall socket | ~270 W peak |
| Price | $4,699 (NVIDIA, after the 2026 increase from $3,999) | $5,499 / €6,599 for the 96 GB, 1 TB M5 Ultra; €10,999 at 256 GB; €20,679 fully maxed (80-core GPU, 256 GB, 16 TB) per ComputerBase. M5 Max Mac Studio from $2,499 / €2,999 (36 GB). 512 GB pricing not yet published. |
| Doubles as a workstation | Yes, DGX OS ships a full Ubuntu desktop over HDMI | Yes, it's a Mac |
Where the Mac genuinely wins: token generation
Decode speed on a single stream is a bandwidth problem, not a compute problem. Every generated token requires reading the active weights out of memory, so 1.2 TB/s beats 273 GB/s and there is no clever software fix. Apple ships a 512 GB configuration; NVIDIA does not ship a 256 GB Spark. If you want a 400 GB model resident in one machine, a high-memory Mac Studio is the only one of these two that can do it, and after the August 25, 2026 refresh that is once again something you can order new. Through the first half of 2026 it wasn't: Tom's Hardware reported that Apple pulled the 512 GB build-to-order option and raised the 256 GB upgrade to $2,000 in March 2026, then cut the 256 GB and 128 GB options entirely, leaving 96 GB as the only M3 Ultra configuration. The M5 Ultra restores the full 96 GB–512 GB ladder, with the 512 GB build arriving in late October 2026 per Apple.
The clearest published head-to-head is EXO Labs' write-up of running both machines together. Those numbers were measured on the previous-generation M3 Ultra Mac Studio (819 GB/s), not the M5 Ultra, which had not shipped when this page was updated. On Llama-3.1 8B at 8K context, the M3 Ultra Mac Studio finished generation in 0.85 s versus the DGX Spark's 2.87 s: the Mac was roughly 3.4x faster at the decode phase, a bit ahead of the 3.0x bandwidth ratio. If that relationship holds, the M5 Ultra's extra ~50% of bandwidth should widen the decode gap further, but nobody has published measured M5 Ultra token rates yet and we are not going to pretend otherwise.
Where the Spark wins: prefill and long prompts
Flip to the other half of that same EXO benchmark, again on M3 Ultra hardware, and the picture inverts. Prefill, reading your prompt, is compute-bound, and the Spark's Blackwell tensor cores dominate Apple's GPU: 1.47 s on the Spark versus 5.57 s on the M3 Ultra, about 3.8× faster. This is the axis where the M5 generation is most likely to have moved: Apple claims up to 1.8× the graphics and up to 4.3× the AI performance of the previous Mac Studio, and the M5 GPU adds per-core Neural Accelerators. Until someone re-runs the benchmark, treat the 3.8× prefill lead as a figure from the old matchup. That is why EXO's headline result was to run both at once, prefill on the Spark and decode on the Mac, for a 2.8× end-to-end speedup over either machine alone.
This matters more than it sounds. RAG pipelines, long-context code assistants, document summarisation and agentic tool loops all replay large prompts constantly. If your typical request is 200 tokens in and 800 out, buy the Mac. If it's 30,000 tokens in and 300 out, the Spark's advantage is the one you actually feel.
MLX vs CUDA, the decision most people should make first
Hardware aside, you're picking an ecosystem.
- MLX (Apple) is excellent, fast-moving, and the community ports popular models quickly. But it's an island: MLX code doesn't run on an H100, and when a brand-new architecture drops, the CUDA implementation lands first and the MLX port arrives days-to-weeks later. Training and fine-tuning support is real but thinner.
- CUDA is the reference implementation of nearly everything. Every paper, every serving framework, every quantisation kernel targets it. A DGX Spark runs the same DGX OS, the same containers and the same CUDA version as the datacenter GPUs you'd rent later, this is what NVIDIA means by the Spark being a development machine rather than a fast inference box.
Honest caveat on the Spark side: it's Arm64 CUDA, and in 2025–26 a handful of Python wheels still assumed x86. That has mostly resolved, and DGX OS ships the stack preconfigured, but it is not literally zero friction.
The community numbers, as reported
- EXO Labs, Llama-3.1 8B @ 8K context, measured on a 256 GB M3 Ultra Mac Studio (the previous generation): Spark prefill 1.47 s / decode 2.87 s; M3 Ultra prefill 5.57 s / decode 0.85 s; combined total 2.32 s vs 4.34 s (Spark alone) and 6.42 s (Mac alone). No equivalent M5 Ultra run exists yet.
- Tom's Hardware's DGX Spark review and NVIDIA's own spec sheet: 128 GB at 273 GB/s, ~1 PFLOP FP4, ~240 W.
- Apple's August 25, 2026 newsroom post and Mac Studio tech specs: M5 Ultra, 96 GB base, up to 512 GB unified memory, 1.2 TB/s; M5 Max, 36 GB base, up to 128 GB, 614 GB/s. Apple says the M5 Ultra "enables users to run enormous LLMs entirely on device" and cites models "with hundreds of billions of parameters", but publishes no tokens/second figure. Corroborated by MacRumors, 9to5Mac and Macworld; euro pricing from ComputerBase.
- Previous generation, for reference: M3 Ultra, 819 GB/s (EXO's test machine was a 256 GB M3 Ultra, per Tom's Hardware). Every Mac benchmark on this page is from that machine.
- Large-model reality check from multiple independent reviewers (ProxPC, LMSYS): on 70B–90B class models at 4-bit the Spark settles around 4–5 tokens/s, and time-to-first-token on a long prompt can run into minutes. The Mac Studio with enough memory is faster here too. Neither machine is a substitute for a rented datacenter GPU when you need throughput.
Price, honestly
At $4,699 the Spark is no longer the $3,999 machine it launched as, but neither is the Mac. The 96 GB Ultra went $3,999 at M3 Ultra launch, $5,299 after Apple's June 2026 increase, and now $5,499 / €6,599 for the M5 Ultra. That base Mac Studio will still out-generate the Spark on models under ~50 GB, and it now does so with 1.2 TB/s behind it. There is also a cheaper entrant worth naming: the M5 Max Mac Studio at $2,499 / €2,999 does 614 GB/s, more than twice the Spark's bandwidth for roughly half the money, though it tops out at 128 GB.
The direction the money goes at the top end has not changed. Big memory on a Mac is still brutally expensive: 256 GB starts at €10,999 and a fully specified 256 GB / 16 TB machine reaches €20,679 (ComputerBase), with 512 GB pricing not yet announced. The Spark's price stops looking odd only when you value the things the Mac cannot do: CUDA parity with production, native 200 GbE clustering of two units into a 256 GB pool, and a headless server you can rack rather than a desktop. If none of those are on your list, the M5 refresh made the Mac a clearly better purchase than it was in July, and we'd rather you knew that now than after the invoice.
Should you wait for an M6 Mac instead? No, and here's why: the M6, Apple's first 2 nm chip, is expected in an entry-level 14" MacBook Pro around October 2026, but per MacRumors' reporting on Apple's roadmap there will be no M6 Pro, Max, or Ultra; the desktop high end stays on the M5 generation until the M7 family arrives in 2027. The M5 Ultra in this comparison is Apple's top desktop silicon for at least the next year.
What we'd tell a friend
- Chat-first, single user, models under 100 GB: Mac Studio.
- Fine-tuning, CUDA libraries, or code that must also run on datacenter GPUs: DGX Spark.
- Long prompts, RAG, agents: Spark, prefill is where it earns its keep.
- Undecided: rent a Spark for an hour first. Run your actual prompts, with your actual context lengths, and read your own numbers instead of ours.