The DGX Spark gives you 128 GB of unified memory at 273 GB/s with NVIDIA's CUDA software stack, while Apple's Mac Studio gives you up to 128 GB at 614 GB/s on M5 Max or up to 512 GB at 1.2 TB/s on M5 Ultra, running MLX and llama.cpp on Metal. Pick the Spark if you need CUDA, and the Mac Studio if you want more memory bandwidth, or more than 128 GB, for local inference.
This is a spec and fit comparison from the vendors' own pages, with no benchmarks of our own. For the Spark in depth, see our DGX Spark guide. For other cards, see Consumer GPUs for AI.
TL;DR
- Memory. Spark: 128 GB LPDDR5x (a 64 GB OEM model is listed as coming soon). Mac Studio M5 Max: 36 GB to 128 GB. M5 Ultra: 96 GB to 512 GB, with the 512 GB option coming in late October 2026 per Apple.
- Bandwidth. Spark 273 GB/s. M5 Max up to 614 GB/s. M5 Ultra 1.2 TB/s. Token generation on large models is bandwidth-bound, so this gap matters.
- Software. Spark runs the CUDA ecosystem on Arm Linux. Mac Studio runs MLX and llama.cpp Metal on macOS, and cannot run CUDA.
- Price. Mac Studio starts at $2,499 (M5 Max) and $5,499 (M5 Ultra). The Spark's price has moved; see below. Compare the configuration with the memory you need, not the starting price.
- Verdict. Spark for CUDA and training-style work you will later move to NVIDIA datacenter GPUs. Mac Studio for the biggest local inference models. Rent a datacenter GPU for fast training or high-throughput serving.
Spec comparison
Spark figures are from NVIDIA's DGX Spark page. Mac Studio figures are from Apple's Mac Studio tech specs. Prices are in US dollars as of October 8, 2026.
| Spec | DGX Spark | Mac Studio (M5 Max) | Mac Studio (M5 Ultra) |
|---|---|---|---|
| Memory | 128 GB LPDDR5x | 36 GB base, up to 128 GB | 96 GB base, up to 512 GB |
| Memory bandwidth | 273 GB/s | 460 GB/s base, up to 614 GB/s (40-core GPU) | 1.2 TB/s |
| CPU | 20-core Arm (10 X925 + 10 A725) | 18 cores | 30 cores, configurable to 36 |
| GPU | Blackwell, 5th-gen Tensor Cores | 32 or 40 cores | 64 or 80 cores |
| AI throughput claim | Up to 1 PFLOP at FP4 (NVIDIA) | Not published as one figure | Not published as one figure |
| Storage | Up to 4 TB NVMe | Per Apple configuration | Per Apple configuration |
| Power | 240 W supply, 140 W chip TDP | Not stated on spec page | Not stated on spec page |
| Size | 150 x 150 x 50.5 mm, 1.2 kg | Larger desktop | Larger desktop |
| Starting price | See below | $2,499 | $5,499 |
Mac Studio starting prices are from Apple's newsroom. We did not find Apple's price for memory upgrades or for the 512 GB configuration, so we do not quote a price for a 128 GB or 512 GB Mac Studio.
For the Spark, NVIDIA's product page does not state a price. The history is covered in our DGX Spark guide: NVIDIA raised the Founders Edition from $3,999 to $4,699 in February 2026, and ServeTheHome reported on October 3, 2026 that the 128 GB model is moving to about $6,950. That is a reported figure, not an NVIDIA price page.
Apple also says four Mac Studio systems clustered over Thunderbolt 5 with RDMA reach up to 3x the inference speed of one system, a claim from Apple's own testing that we have not verified. NVIDIA states that two linked Sparks reach 400 billion parameters.
What fits in memory
Computed, not measured. Weights take about parameters times bytes: FP16 is 2 bytes, INT8 is 1, 4-bit is about 0.5. Unified memory is shared with the operating system and the KV cache, so assume you can use somewhat less than the total, and leave headroom for context.
| Model | Weights at 4-bit | Spark 128 GB | M5 Max 128 GB | M5 Ultra 96 GB | M5 Ultra 512 GB |
|---|---|---|---|---|---|
| 70B | about 35 GB | Yes | Yes | Yes | Yes |
| 70B at FP16 | about 140 GB | No | No | No | Yes |
| 120B | about 60 GB | Yes | Yes | Yes | Yes |
| 200B | about 100 GB | Tight but yes | Tight but yes | No | Yes |
| 405B | about 203 GB | No | No | No | Yes |
| 671B | about 336 GB | No | No | No | Yes |
NVIDIA's own claim is that one Spark runs inference on models up to 200 billion parameters and fine-tunes models up to 70 billion. Sizes such as 405B and 671B are public parameter counts of the Llama 3.1 405B and DeepSeek-V3 models; the memory numbers are our arithmetic.
Speed: the bandwidth ceiling
For a dense model, each generated token reads about all the weights, so tokens per second cannot exceed bandwidth divided by weight size. For a 35 GB model (70B at 4-bit), computed ceilings are:
- Spark at 273 GB/s: about 7.8 tokens per second.
- M5 Max at 614 GB/s: about 17.5 tokens per second.
- M5 Ultra at 1.2 TB/s: about 34 tokens per second.
These are theoretical upper bounds, not benchmarks; real speeds are lower. Mixture-of-experts models read only the active experts per token, so they run faster than this ceiling suggests for a given memory footprint. See mixture of experts and unified memory. Prompt processing is a different, compute-bound phase. The Spark's Blackwell GPU has dedicated Tensor Cores for it, and Apple claims prompt processing in LM Studio up to 3.9x faster on M5 Max than M4 Max (Apple's own testing). We do not know of a neutral head-to-head, so we do not quote one.
Software: CUDA versus MLX and Metal
DGX Spark. The CUDA stack. PyTorch, vLLM, SGLang, TensorRT-LLM and the fine-tuning tools in our fine-tuning frameworks overview target CUDA, and a container built here moves to an NVIDIA datacenter GPU. The caveat is Arm Linux: some packages ship x86 wheels only.
Mac Studio. MLX is Apple's array framework for Apple silicon, with Python, C++, C and Swift APIs. It needs Apple silicon and macOS 14.0 or later, and installs with:
pip install mlx
llama.cpp treats Apple silicon as a first-class citizen with a Metal backend that is on by default on macOS, per its README. This is the quickest start there:
llama serve -hf ggml-org/Qwen3.5-0.8B-GGUF
Swap in a GGUF that fits your memory. See the llama.cpp guide and What is Ollama for more. Mac Studio cannot run CUDA-only code, so CUDA libraries without a Metal or MLX port are out.
Who should buy which
- Choose the DGX Spark if your work ends on NVIDIA GPUs: prototyping a training or serving stack you will later run on an H100 or similar, with the same CUDA container.
- Choose the Mac Studio if you want to run the biggest models locally for chat and coding, you value bandwidth, and your tools run on Metal or MLX.
- Rent a datacenter GPU for training runs, fine-tuning above 70B, or serving many users, where HBM bandwidth and throughput beat any desktop box. See Datacenter GPUs in 2026.
Rent today
Test your workload on a CUDA workstation or consumer card before you commit to hardware. Aquanode manages and optimizes GPUs for training and inference workloads, and the live box shows what is available now.
FAQ
Is the DGX Spark faster than a Mac Studio?
For token generation on large models, bandwidth sets the ceiling, and the Mac Studio has more (614 GB/s on M5 Max and 1.2 TB/s on M5 Ultra against 273 GB/s). The Spark has CUDA and FP4 Tensor Cores. We have no neutral benchmark, so we do not claim a winner on speed.
Can a Mac Studio run CUDA?
No. It runs MLX, llama.cpp with Metal and other Apple silicon software.
How much memory can each hold?
The Spark has 128 GB. The Mac Studio M5 Max goes up to 128 GB and the M5 Ultra up to 512 GB, with the 512 GB option arriving in late October 2026 per Apple.
Which is better for fine-tuning?
The Spark, because the CUDA fine-tuning tools target it directly. NVIDIA says it fine-tunes models up to 70B on 128 GB. MLX also supports training on Apple silicon, but the tool list is smaller.
Should I buy either instead of renting?
If you need it daily and keep data local, owning pays back. For occasional large runs, renting is cheaper to start. Our DGX Spark guide covers the break-even.
