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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Neither DGX Spark nor Mac Studio is the universal choice for local LLM inference. DGX Spark is the clearer fit if you want NVIDIA’s CUDA and DGX software path; the 2025 Mac Studio’s cited M4 Max and M3 Ultra configurations list higher memory bandwidth. In a July 2026 llama.cpp comparison, the tested M4 Max generated tokens faster than the tested GB10 system, but prompt-processing results varied by workload. Choose by exact configuration, model and context needs, and software ecosystem—not by one headline specification.
How the configurations compare
| System | Memory | Listed memory bandwidth | Inference path in the cited material |
|---|---|---|---|
| DGX Spark | 128 GB unified system memory | 273 GB/s | DGX OS and NVIDIA’s documented CUDA-enabled llama.cpp workflow |
| Mac Studio with M4 Max | Varies by configuration; confirm the specific build | 546 GB/s | Apple silicon; llama.cpp was used in the cited independent comparison |
| Mac Studio with M3 Ultra | Varies by configuration; confirm the specific build | 819 GB/s | Apple silicon; the cited M4 Max benchmark does not establish M3 Ultra performance |
DGX Spark is a Grace Blackwell system with a 20-core Arm CPU and 128 GB of LPDDR5x unified memory, according to NVIDIA’s hardware specifications. Apple’s 2025 Mac Studio technical specifications give the M4 Max and M3 Ultra bandwidth figures above. Apple’s 2025 launch announcement describes configurations with up to 40 GPU cores for M4 Max and up to 80 for M3 Ultra.
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These figures describe different things. Memory capacity helps determine whether a model, its runtime needs, and its context-related KV cache can fit. Bandwidth can matter for inference speed, especially token generation, but it does not by itself predict performance for every model or software stack.
What the benchmark says—and what it does not
Tom’s Hardware’s July 30, 2026 comparison tested llama.cpp on an M4 Max and a GB10 system with four-bit quantizations of Qwen 3.6-35B-A3B, Gemma 4 12B, and gpt-oss-120b. The tested M4 Max had higher generation throughput across the reported models and context depths. Prompt processing was less one-sided: GB10 led in some tested conditions.
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That is evidence for those systems, models, quantizations, context depths, and llama.cpp setup—not a ranking for every local inference workload. In particular, it does not establish how an M3 Ultra compares with DGX Spark. A result on one Mac Studio configuration should not be carried over to another without a matching test.
Capacity is not the same as speed
A model’s weights are only part of the memory calculation. Runtime overhead and the KV cache also use memory, and cache demand can grow with context length. NVIDIA’s llama.cpp walkthrough explicitly accounts for memory for the model and cache. Its example asks for about 30 GB of free RAM for the model used in that walkthrough, plus disk space for the download and build artifacts; that example is not a general requirement for other models.
For a useful comparison, first identify the model, quantization, and context length you intend to run. Then check whether the exact system configuration leaves enough memory for weights, cache, and runtime overhead. Only after the model fits should you compare throughput for that workload.
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Software ecosystem and setup
DGX Spark: a documented CUDA route
NVIDIA provides an official DGX Spark llama.cpp guide covering a CUDA build, downloading a GGUF checkpoint, GPU offload, and serving chat through llama-server’s OpenAI-compatible API. The guide’s stated walkthrough uses MTP-enabled Qwen3.6-35B-A3B as its hands-on example; that describes the guide, not a general performance claim. This documented path is relevant if your development work already depends on CUDA or NVIDIA tooling.
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“Mac Studio” does not name one fixed inference configuration: M4 Max and M3 Ultra have different listed bandwidth, and memory depends on the selected build. The cited independent test shows that llama.cpp can be used for local inference on an M4 Max, while Apple’s launch announcement describes the platform’s on-device AI capabilities. If you are choosing a Mac for a particular model, verify the memory configuration and the inference software you plan to use rather than assuming all Mac Studio builds behave alike.
Which one should you choose?
- Choose DGX Spark when CUDA and NVIDIA’s documented DGX workflow are central to your work, and its 128 GB unified-memory capacity suits the model and context you need.
- Consider Mac Studio with M4 Max when you want an Apple-silicon system and the cited llama.cpp generation-throughput result is relevant to your workload. Treat that test as scoped evidence, not a guarantee.
- Consider Mac Studio with M3 Ultra when its 819 GB/s listed bandwidth and the specific memory configuration fit your needs. The cited comparison does not provide a matched M3 Ultra-versus-GB10 result, so do not infer a winner from the M4 Max test.
Prices, regional inventory, and exact purchasable configurations are not established here. Check current listings for your region before deciding between builds.
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