Overview
Mac Studio vs DGX Spark comes down to memory and bandwidth on paper: the Mac Studio with M5 Ultra lists 96 GB to 512 GB of unified memory at 1.2 TB/s, while the DGX Spark lists 128 GB at 273 GB/s, and no measured speed for either is published here. Both keep the model in one memory pool shared by CPU and GPU (see unified-memory-vs-vram). This page compares only what Apple and NVIDIA print on their own pages, labels vendor performance claims as claims, and marks the rest unknown. Access dates are under Sources.
Mac Studio vs DGX Spark: what do the spec pages say?
The Mac Studio spec page lists two chips, M5 Max and M5 Ultra, and NVIDIA lists one DGX Spark built on the GB10 Grace Blackwell Superchip.
| Spec | Mac Studio, M5 Max | Mac Studio, M5 Ultra | NVIDIA DGX Spark |
|---|---|---|---|
| CPU | 18-core (6 super, 12 performance) | 30-core (10 super, 20 performance) | 20-core Arm (10 Cortex-X925, 10 Cortex-A725) |
| GPU | 32-core (40-core configurable) | 64-core (80-core configurable) | Blackwell; core count not listed |
| Memory | 36 GB base; 48, 64 or 128 GB | 96 GB base; 256 or 512 GB | 128 GB LPDDR5x unified |
| Memory bandwidth | 460 GB/s (614 GB/s with 40-core GPU) | 1.2 TB/s | 273 GB/s (256-bit interface) |
| Storage | 512 GB base, up to 8 TB | 1 TB base, up to 16 TB | Up to 4 TB NVMe |
| Fast links | 4 Thunderbolt 5, 10Gb Ethernet | 4 Thunderbolt 5, 10Gb Ethernet | 10 GbE plus ConnectX-7 NIC at 200 Gbps |
| Size / weight | 7.7 x 7.7 x 3.7 in; 6.0 lb | same; 8.0 lb | 150 x 150 x 50.5 mm; 1.2 kg |
| Power | not on the spec page | not on the spec page | 240 W supply; GB10 TDP 140 W |
NVIDIA also lists a 64 GB option sold only through OEM partners. Apple’s newsroom gives U.S. prices of 5,499 (M5 Ultra), with the 512 GB configuration arriving in late October (Apple newsroom). NVIDIA’s page lists no DGX Spark price, so that price is unknown here. See mac-studio-llm and m5-ultra-llm for the Mac side.
Which one has more memory bandwidth?
On the printed numbers the Mac Studio is higher at every tier, and the gap is plain division:
- M5 Max, 460 / 273 = 1.68
- M5 Max with 40-core GPU, 614 / 273 = 2.25
- M5 Ultra, 1,200 / 273 = 4.40 (taking 1.2 TB/s as 1,200 GB/s)
Bandwidth is a spec, not a speed. Turning it into a generation rate depends on the runtime, quantization and context, and this site publishes no such figures. Background is on apple-silicon-memory-bandwidth.
Which one holds bigger models?
Capacity favors the Mac Studio at the top end: 512 GB against 128 GB per box. NVIDIA’s own table claims, as vendor claims:
| DGX Spark configuration | Memory | NVIDIA’s claimed max model size |
|---|---|---|
| 1x (64 GB, OEM only) | 64 GB | up to 100B parameters |
| 1x | 128 GB | up to 200B parameters |
| 2x | 256 GB total | up to 400B parameters |
| 4x | 512 GB total | up to 700B parameters |
NVIDIA’s page does not say which precision those limits assume. To check them yourself, use the methodology formula weights_GB = params x bits / 8 / 1e9. At 4.0 bits per weight, 200B parameters is 200e9 x 4 / 8 / 1e9 = 100 GB, and 700B is 350 GB. Add the KV cache (which depends on the model’s config.json) and the 2 GB overhead assumption from methodology. Weights alone then sit under 128 GB for 200B and under 512 GB for 700B, but how much of that memory the GPU may actually use is unknown for both machines. Apple’s limit is covered on mac-gpu-memory-limit. The same 4-bit arithmetic puts a 70B model at 35 GB of weights, which is why 96 GB, 128 GB and larger tiers matter; the longer treatment is how-much-ram-for-70b-model.
What is the difference between prefill and decode?
Prefill is the model reading your prompt, and decode is the model writing the answer one token at a time. Prefill processes many prompt tokens at once, so it tends to be limited by compute. Decode must read the model weights for each new token, so it tends to be limited by memory bandwidth. This is a general description, not a measurement of either machine.
That is why “M5 Ultra vs DGX Spark prefill” is a different question from bandwidth. The vendor claims point at compute and cannot be compared directly:
- NVIDIA: “up to 1 petaFLOP of AI performance at FP4 precision” (vendor claim).
- Apple: M5 Max prompt processing up to 3.9x faster than M4 Max in LM Studio, and M5 Ultra up to 4x faster than M3 Ultra (Apple claims from its July 2026 testing; Apple does not list the model or test configuration). Apple’s newsroom gives no FLOPS figure for the M5 Ultra, and the Neural Engine is listed as 32 cores. See m5-neural-accelerators.
Both claims are relative or peak figures. Neither is a prefill number for the same model on both machines, so a head-to-head prefill result is unknown.
Which is faster for local LLMs?
Unknown. No primary source read for this page measures the same model on a Mac Studio and a DGX Spark, and this site publishes no tokens-per-second figures. To get an answer for your model, follow measure-tokens-per-second-mac on the Mac and run the same prompt on the other box. Rankings from other sites are not repeated here.
Can you cluster them?
Both vendors say yes. NVIDIA says ConnectX networking lets up to four DGX Spark systems connect for larger models, and its table above lists 4x at 512 GB. Apple claims a cluster of four Mac Studio systems delivers up to 3x faster AI inference than a single system (vendor claim). Apple’s pages here do not state which model or software that claim used. See mac-llm-cluster.
How do software and ecosystem differ?
The DGX Spark ships with NVIDIA DGX OS, so it is an NVIDIA GPU stack. A Mac Studio runs macOS with Apple’s GPU and Neural Engine, which is the stack that tools such as Ollama, LM Studio and MLX target on this site. Which model runtimes support which machine is a question for each project’s docs, and this page does not rank them. The wider NVIDIA-versus-Mac view is mac-vs-nvidia-for-local-llm, and the discrete-GPU side is best GPU for local LLM on the sister site.
Questions
Mac Studio vs DGX Spark for local LLM, which should I buy?
Pick by the model you need to hold. The DGX Spark lists 128 GB at 273 GB/s; the Mac Studio lists up to 128 GB (M5 Max, 460 to 614 GB/s) or up to 512 GB (M5 Ultra, 1.2 TB/s). Speed on your model is unknown without a test, and the DGX Spark price is not on NVIDIA’s page.
M5 Ultra vs DGX Spark, which has more memory bandwidth?
The M5 Ultra: 1.2 TB/s on Apple’s spec page against 273 GB/s on NVIDIA’s, a ratio of about 4.40. Bandwidth is a spec, not a measured speed.
Which is better for LLM, DGX Spark or Mac Studio?
Neither is shown better overall by the sources read. The Mac Studio lists more memory and bandwidth; the DGX Spark lists a 200 Gbps ConnectX-7 NIC and an NVIDIA GPU stack. No same-model speed comparison is published here.
M5 Ultra vs DGX Spark prefill: which is faster?
Unknown. NVIDIA claims up to 1 petaFLOP at FP4 and Apple claims up to 4x faster LLM prompt processing than M3 Ultra, but those are different kinds of claim and no source read compares them on one model.
How much memory does the DGX Spark have?
128 GB of LPDDR5x coherent unified system memory on a 256-bit interface at 273 GB/s, per NVIDIA. A 64 GB version exists only through OEM partners.
Can a DGX Spark run a 200B model?
NVIDIA claims up to 200B parameters on one 128 GB unit (vendor claim, precision not stated). At 4.0 bits per weight the weights are 100 GB by params x bits / 8 / 1e9, before KV cache and overhead.
What is the maximum memory on a Mac Studio?
512 GB on the M5 Ultra, arriving in late October per Apple’s newsroom. M5 Max goes to 128 GB.
Does this page give tokens per second?
No. This site publishes none, and speed for these two machines is unknown here.
Sources read on 2026-10-13
- Mac Studio tech specs: chips, core counts, memory options, bandwidth, storage, ports, dimensions, weight, Neural Engine cores.
- Apple newsroom, new Mac Studio with M5 Max and M5 Ultra: U.S. prices, availability, 512 GB timing, Apple’s AI, prompt-processing and clustering claims (vendor claims, July 2026 Apple testing).
- NVIDIA DGX Spark: GB10 superchip, CPU cores, 128 GB and 273 GB/s, 1 petaFLOP FP4 claim, model size claims, networking, size, power, OS; no price listed.
- methodology: weight and KV formulas and the 2 GB overhead assumption.