For fitting the largest local LLM, the 2026 Mac Studio with M5 Ultra is the stronger choice on paper: it can be configured with up to 512 GB of unified memory, compared with 128 GB in NVIDIA DGX Spark. Choose DGX Spark when CUDA compatibility and NVIDIA’s documented llama.cpp/GGUF workflow matter more. Neither system’s published specifications establish which will generate tokens faster for your particular model.
At a glance: the configurations that matter
| System | Maximum unified memory | Published memory bandwidth | Storage options | What the figures mean for local LLMs |
|---|---|---|---|---|
| NVIDIA DGX Spark | 128 GB LPDDR5x coherent unified system memory (NVIDIA product page) | 273 GB/s (NVIDIA DGX Spark User Guide) | 1 TB or 4 TB NVMe (NVIDIA hardware guide) | Fixed 128 GB memory ceiling; NVIDIA documents a CUDA-based llama.cpp workflow for GGUF models. |
| Mac Studio with M5 Max | Up to 128 GB (Apple technical specifications) | Up to 614 GB/s with the 40-core GPU option (Apple technical specifications) | Up to 8 TB (Apple technical specifications) | Memory capacity can match Spark, but the hardware and software path differ. |
| Mac Studio with M5 Ultra | Up to 512 GB (Apple technical specifications) | Up to 1.2 TB/s with the 80-core GPU option (Apple technical specifications) | Up to 16 TB (Apple technical specifications) | The highest stated memory ceiling here, allowing more room for larger models or longer context, subject to runtime needs. |
Apple announced the M5 Max and M5 Ultra Mac Studio generation on August 25, 2026. These figures describe configurations, not a matched local-inference test. The model, quantization, runtime, context length and serving settings can all change the result.
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Which one can run the bigger model locally?
The M5 Ultra Mac Studio can accommodate the larger memory footprint: its maximum 512 GB unified memory is four times DGX Spark’s 128 GB. That is the clearest hardware distinction if your priority is keeping a large model on one desktop system. An M5 Max tops out at 128 GB, the same nominal capacity as Spark, so the Ultra comparison should not be generalized to every Mac Studio configuration.
Memory capacity is not all available for model weights. Weights, the inference runtime, and the KV cache—which stores information used during generation—share system memory. A long context or more concurrent requests can increase cache needs, leaving less room for weights. NVIDIA’s llama.cpp guide makes the qualification explicit: “DGX Spark supports any GGUF format model checkpoint with llama.cpp, as long as the system has memory available to host and run the checkpoint.” The same practical constraint applies when sizing any local setup: leave headroom rather than treating the advertised maximum as a weight-only budget.
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When DGX Spark is the better fit
You need CUDA-oriented software
DGX Spark is the more straightforward choice when your development or deployment depends on NVIDIA’s CUDA ecosystem. NVIDIA publishes a guide for building llama.cpp with CUDA, loading GGUF model weights, and serving chat through llama-server’s OpenAI-compatible HTTP API. That is a documented route from setup to local inference, rather than a general guarantee that every model or feature works identically.
Your target model fits within its usable memory
At 128 GB, Spark can support many local workloads, but whether a particular checkpoint fits depends on its weight size, quantization, runtime overhead and context/KV-cache requirements. Check the full memory requirement for the exact model and configuration before choosing it. If the workload exceeds available memory, the headline system-memory figure does not make it fit.
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When Mac Studio is the better fit
You need the highest stated memory ceiling
If the workload needs more than 128 GB for model weights plus runtime and context, the M5 Ultra’s up-to-512-GB configuration is the more capable option for keeping a larger model in unified memory. The M5 Max is a different case: its maximum is 128 GB, so it does not offer that capacity advantage over Spark.
Your chosen inference software supports Apple silicon
Apple’s specifications establish the Mac hardware configurations, but do not verify compatibility for every LLM runtime, backend or feature on this generation. Before buying, check that the exact software stack you intend to use supports Apple silicon and the operations your workload needs. Do not assume a CUDA-focused workflow transfers directly to Mac.
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Do the bandwidth and AI-compute figures predict token speed?
No. The published bandwidth figures—273 GB/s for Spark, up to 614 GB/s for M5 Max, and up to 1.2 TB/s for M5 Ultra—are useful hardware specifications, but they are not a substitute for a benchmark using the same model and settings. Likewise, NVIDIA’s “up to 1 petaflop” AI-compute claim is tied to FP4 and fifth-generation Tensor Cores; Apple’s announcement says “up to 4.3x faster AI performance” with its own comparison details and footnotes. These vendor claims use different formats, baselines and methods, so they do not establish a head-to-head LLM speed winner.
Inference speed also has more than one part. Prompt processing, or prefill, handles the input; decode produces the output tokens. A system can behave differently across those phases, and interactive single-user use is not the same workload as concurrent serving or fine-tuning. The official materials cited here do not provide a matched current-generation M5 Mac Studio versus DGX Spark result.
Rank #4
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- FITS PERFECTLY IN YOUR SPACE — The all-in-one desktop design is strikingly thin, comes in seven vibrant colors, and elevates any space with style.
- BUILT FOR APPLE INTELLIGENCE — Apple Intelligence is the personal intelligence system that helps you write, express yourself, and get things done effortlessly. With groundbreaking privacy protections, it gives you peace of mind that no one else can access your data — not even Apple.*
- SUPERCHARGED BY M4 — Get more done faster with the Apple M4 chip. From editing photos to creating presentations to gaming, you’ll fly through work and play.
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How to compare them for your workload
- Fix the model and checkpoint. Compare the same model, weight format and quantization on both systems; different sizes or quantizations are not an apples-to-apples test.
- Set the same context and workload. Use the same context length, prompt, output length, batch settings and concurrency. Note whether you care about prefill, decode, or both.
- Use the runtime you would actually deploy. Record its version, backend and relevant settings. Confirm the Mac build supports the features you need; for Spark, follow the documented CUDA llama.cpp/GGUF path if it matches your workflow.
- Check memory headroom as well as whether the model loads. Include runtime allocations and KV cache at your intended context and concurrency. A run that only fits with little room may not represent your real use.
- Measure both phases on the exact configurations you can buy. Record prompt-processing and generated-token rates separately. The official specifications do not settle this comparison for your chosen model.
Price, availability, and other desktop trade-offs
There is no verified current regional price or inventory comparison here, and both product families vary by memory and storage configuration. Compare the exact M5 Max, M5 Ultra or DGX Spark configuration available in your region at the time you plan to buy; a price comparison between differently equipped systems can mislead. Power use, noise and peripheral needs were not established by the specifications cited above, so those should be checked against independent measurements or your own requirements rather than inferred from bandwidth or compute claims.
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- NVIDIA DGX Spark specifications: NVIDIA DGX Spark product page and hardware guide.
- NVIDIA’s documented local inference workflow: DGX Spark llama.cpp guide.
- Apple Mac Studio configurations: Apple technical specifications and August 25, 2026 announcement.
This comparison concerns the 2026 M5 Max and M5 Ultra Mac Studio generation and the DGX Spark specifications cited above. Older Mac Studio generations are not direct substitutes for these configurations, and no independent matched benchmark is presented.
Quick Recap
Best Value
- BRAWN OF A NEW AGE — Mac Studio is a tremendously powerful pro desktop. The M5 Max chip enables remarkable on-device AI compute. Blast through creative projects and professional workflows with the advanced graphics architecture and faster memory and storage.
- M5 MAX CHIP — Tap into breakthrough performance with a next-generation CPU, a more powerful GPU with third-generation ray tracing, and a Neural Accelerator built into each GPU core. Mac Studio gets a boost with more power to generate real-time media and accelerate complex workflows.
- MEMORY AND STORAGE — Get up to 128GB unified memory and up to 614GB/s memory bandwidth for more speed when processing massive datasets, complex 3D scenes, and inference in AI workflows. And up to 2x faster storage* expedites tasks like file transfers and loading large projects.
- A POWERFUL PLATFORM FOR AI — Apple silicon, and every major component that powers it, is designed to run demanding AI workflows like running huge LLMs, directly on device. And Apple Intelligence* helps you write, express yourself, and get things done effortlessly, while Siri AI* is your profoundly capable assistant — all with groundbreaking privacy protections.
- A POWERFUL PLATFORM FOR AI — Apple silicon, and every major component that powers it, is designed to run demanding AI workflows like running huge LLMs, directly on device.
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