Exploring the 512GB Apple-silicon path? See whether a 512GB Mac Studio can run Muse Spark Open, including the published 304GB Q2_K and 344GB oQ3e tests and why they do not replace the official 700GB NVFP4 requirement.
Short answer: choose DGX Spark when you need NVIDIA’s CUDA-oriented DGX software lane in a compact 128GB Arm Linux system. Choose Mac Studio when your workflow is supported by Metal or MLX, macOS is the preferred environment, or an M5 Ultra configuration above 128GB changes what can fit. Both use unified system memory shared by CPU, GPU, operating system and applications; neither advertised total is dedicated VRAM.
The decision in one minute
- DGX Spark: 128GB LPDDR5x coherent unified memory, 273GB/s, GB10 Grace Blackwell and DGX OS on Arm.
- M5 Max Mac Studio: 36GB to 128GB unified memory, with 460GB/s or 614GB/s depending on configuration.
- M5 Ultra Mac Studio: 96GB, 256GB or 512GB unified memory and 1.2TB/s published bandwidth.
- Do not choose from capacity alone: confirm the exact model format, runtime, extensions, context, multimodal path and measured workload.
DGX Spark vs Mac Studio specifications that matter
| System | Unified memory | Published bandwidth | Software lane | System role |
|---|---|---|---|---|
| NVIDIA DGX Spark | 128GB LPDDR5x coherent system memory | 273GB/s | CUDA stack on DGX OS / Arm Linux | Complete compact GB10 development system |
| Mac Studio M5 Max | 36GB; configurable to 48GB, 64GB or 128GB | 460GB/s base; 614GB/s on the 40-core GPU configuration | Metal, MLX and supported macOS applications | Complete compact desktop |
| Mac Studio M5 Ultra | 96GB; configurable to 256GB or 512GB | 1.2TB/s | Metal, MLX and supported macOS applications | Complete compact desktop |
The 128GB comparison: equal label, different system
DGX Spark and the 128GB M5 Max share an advertised capacity number, but that does not make them interchangeable. Both totals are consumed by the operating system, applications, CPU tasks, GPU tasks, model weights, KV cache and workspaces. DGX Spark targets NVIDIA’s CUDA ecosystem on Arm Linux; Mac Studio targets Apple’s Metal and MLX paths on macOS. A compatible model artifact on one platform may need conversion or may not be supported on the other.
Exact Qwen3.5 file boundaries
Margins below subtract cited decimal file size from nominal system memory. They are file-only arithmetic, not usable-memory claims. Shared OS and runtime allocations make every real margin smaller.
| Artifact | Files | 128GB DGX Spark / M5 Max | M5 Ultra 96GB | M5 Ultra 256GB | M5 Ultra 512GB |
|---|---|---|---|---|---|
| Qwen3.5 35B BF16 | 71.904GB | 56.096GB nominal | 24.096GB nominal | 184.096GB nominal | 440.096GB nominal |
| 122B official GPTQ-Int4 | 78.864GB | 49.136GB nominal | 17.136GB nominal | 177.136GB nominal | 433.136GB nominal |
| 122B Q4_K_M + projector | 77.446GB | 50.554GB nominal | 18.554GB nominal | 178.554GB nominal | 434.554GB nominal |
| 122B official FP8 | 127.163GB | 0.837GB — unsafe | No file fit | 128.837GB nominal | 384.837GB nominal |
| 122B Q8_0 + projector | 130.781GB | No file fit | No file fit | 125.219GB nominal | 381.219GB nominal |
| 122B official BF16 | 250.173GB | No file fit | No file fit | 5.827GB — unsafe | 261.827GB nominal |
| 397B official GPTQ-Int4 | 235.708GB | No file fit | No file fit | 20.292GB — validate headroom | 276.292GB nominal |
| 397B Q4_K_M + projector | 245.012GB | No file fit | No file fit | 10.988GB — tight boundary | 266.988GB nominal |
| 397B official FP8 | 406.152GB | No file fit | No file fit | No file fit | 105.848GB nominal |
| 397B Q8_0 + projector | 422.426GB | No file fit | No file fit | No file fit | 89.574GB nominal |
| 397B official BF16 | 806.796GB | No file fit | No file fit | No file fit | No file fit |
Where the capacity decision changes
- At 96GB: the listed 122B 4-bit artifacts pass the file gate, but shared-memory headroom must be validated.
- At 128GB: the 127.163GB FP8 file leaves only 0.837GB before the OS or runtime and is not a safe plan; the 130.781GB Q8-plus-projector file does not fit.
- At 256GB: 122B BF16 leaves only 5.827GB and the 397B Q4-plus-projector pair leaves 10.988GB; both are unsafe/tight shared-memory boundaries.
- At 512GB: the listed 397B FP8 and Q8-plus-projector artifacts become one-system capacity candidates, subject to runtime support and measured peak use.
CUDA on Arm Linux or Metal/MLX on macOS?
DGX Spark is the direct path when the required framework, container and kernels support its NVIDIA Blackwell GPU and Arm64 host. Do not assume an x86 CUDA binary, Python wheel, extension or container works unchanged. Mac Studio is the direct path when the required application or model is supported through MLX, Metal or another Apple-silicon backend. More unified memory cannot fix an unsupported architecture, quantization or multimodal projector.
Bandwidth, power and multi-system safeguards
- Do not turn 273GB/s, 460/614GB/s or 1.2TB/s into predicted tokens per second. Runtime kernels, model shape, context, batch and thermal behavior matter.
- DGX Spark’s 140W GB10 TDP and Apple’s complete-system power figures describe different scopes; compare measured wall power under the same workload.
- DGX Spark supports documented ConnectX-7 multi-system configurations, but aggregate memory is not one automatic transparent pool.
- Mac Studio memory and GPU are not upgradeable after purchase; choose capacity only after validating the complete workload.
Choose the next evidence
| If this is your constraint | Start here |
|---|---|
| You require CUDA and want the 128GB compact-system class | DGX Spark and GX10 guide |
| You require macOS, MLX or more than 128GB | Mac Studio memory guide |
| You are evaluating the 256GB/512GB M5 Ultra tier | M5 Ultra capacity guide |
| You need a repeatable pass/fail workload | Local-AI workload test |
Continue the decision
- Qwen3.5 35B memory · 122B-A10B memory · 397B-A17B memory
- 96GB-to-1TB hardware paths · memory calculator
- Screened offers · benchmark builder · source-reported database
- Save a private watchlist after confirming the platform and capacity.
Primary sources
- NVIDIA DGX Spark specifications
- NVIDIA DGX Spark hardware guide
- Apple Mac Studio M5 Max/M5 Ultra technical specifications
- Apple MLX repository
Reviewed October 25, 2026. Specifications are manufacturer-published. HighVRAM has not independently benchmarked these systems as a controlled pair. File fit does not establish compatibility, speed, quality, reliability or value.
Dedicated-VRAM alternative: compare DGX Spark with RTX PRO 6000 across 128GB shared memory, 96GB ECC VRAM and CUDA platform requirements.
DGX Spark alternatives: Compare the wider alternative set after choosing the software lane in the constraint-first comparison hub.
If a replaceable x86 desktop GPU is also under consideration, see DGX Spark vs RTX 4090.
If used 24GB x86 hardware is also under consideration, compare DGX Spark vs RTX 3090.