Developers who weigh a Mac Studio M5 Ultra against an NVIDIA DGX Spark usually have three practical reasons. The Mac has more than four times the memory bandwidth, so text generation is faster. It can be configured with 256 GB or 512 GB, where the Spark stops at 128 GB. And it is a complete, near-silent desktop they can also use for everything else. The Spark has strong reasons of its own. It runs the same CUDA software as NVIDIA's data-centre GPUs, so code moves to production unchanged. It has far better fine-tuning and multi-user serving tools, and it has built-in 200 Gb/s networking for clustering. The choice comes down to whether your work ends on your desk or on an NVIDIA server.
What are developers actually comparing?
| NVIDIA DGX Spark | Mac Studio M5 Ultra 96 GB | Mac Studio M5 Ultra 256 GB | |
|---|---|---|---|
| Memory | 128 GB unified (about 119 GB usable) | 96 GB unified (about 72 GB GPU by default) | 256 GB unified (about 192 GB GPU by default) |
| Memory bandwidth | 273 GB/s | 1.2 TB/s | 1.2 TB/s |
| Operating system | DGX OS (Ubuntu-based Linux) | macOS | macOS |
| AI software | Full CUDA stack: PyTorch, TensorRT-LLM, vLLM, SGLang, NVIDIA playbooks | MLX, mlx-lm, llama.cpp, LM Studio, Ollama (MLX preview), vllm-metal | Same as 96 GB |
| Networking | ConnectX-7, two QSFP ports up to 200 Gb/s each | Six Thunderbolt 5 ports; 10GbE | Same as 96 GB |
| Power | 240 W external supply; roughly 22–45 W idle depending on firmware and maker | 9 W idle, 385 W maximum (Apple) | Same as 96 GB |
| Price | S$9,300 for 4 TB at SourceIT (listed 8 Nov 2026) | S$7,999, Apple Singapore's list price (30-core CPU, 64-core GPU) | About S$16,699 with 2 TB, calculated from Apple Singapore's upgrade pricing (confirm on apple.com/sg) |
Source: NVIDIA DGX Spark documentation and user guide; Apple Mac Studio specifications and Apple Support 102027 (21 Sep 2026); idle power from Tom's Hardware (Feb 2026) and ServeTheHome (Oct 2025). The 75% GPU memory default on macOS is Metal's standard working-set limit; it can be raised with an unsupported system setting. Prices change; ask for a current quotation.
Why does memory bandwidth matter so much to developers?
When a language model writes text, it reads its active weights from memory for every token. Generation speed is therefore limited by memory bandwidth, and that is the M5 Ultra's biggest technical advantage over GB10, the chip inside DGX Spark: 1.2 TB/s against 273 GB/s.
The published numbers point the same way, with a caveat. On gpt-oss-120b, the official llama.cpp benchmark for DGX Spark (February 2026) shows about 59 tokens per second of generation. A single community run on a 256 GB M5 Ultra using the oMLX engine reported about 110. Tom's Hardware also found the M5 Ultra ahead of DGX Spark on both prompt processing and generation, but not with the same engine on both machines. No identical-engine head-to-head has been published, so treat the gap as directional.
For a developer iterating on prompts, agents or coding assistants all day, faster generation is a quality-of-life gain. It isn't decisive for every workload, though: prompt processing on long inputs is compute-bound, and NVIDIA keeps its edge in raw tensor throughput there.
Is memory capacity the bigger reason?
For some teams, yes. DGX Spark is fixed at 128 GB. That runs 120B-class mixture-of-experts models at 4-bit comfortably, and NVIDIA's own multi-agent playbook loads gpt-oss-120b plus two smaller models into about 120 GB. A 235B-class model, a 671B model at 4-bit, or several large models kept loaded at once needs more. The Mac Studio M5 Ultra offers 256 GB and 512 GB, the latter orderable since 28 October 2026.
NVIDIA's answer is clustering. Two Sparks linked over ConnectX-7 give 256 GB, which NVIDIA says handles models of about 400 billion parameters, and its Cluster Assistant software supports up to four units. Macs can also be clustered, using RDMA over Thunderbolt 5. That needs macOS 26.2 or later, direct cables (no switch) and a setting enabled from Recovery mode, and a full mesh tops out at four machines. Both approaches work, but one large Mac is simpler than two or four small boxes. Our post on one Mac Studio or two RTX Spark PCs looks at that trade-off in detail.
Is a Mac simply easier to live with?
This is the reason developers mention most often, and it's the hardest to measure. DGX Spark is a Linux appliance designed to be reached over the network or used with a monitor as a development box. A Mac Studio is also a full macOS desktop: Xcode, design and video apps, office software and the developer's usual tools all run on the same machine as the models.
Setup friction comes up in user reports. One tester quoted by Spheron said the Mac (an earlier M3 Ultra) was serving inference four hours after being plugged in, while two DGX Sparks took four days. That is a single, unverified anecdote, not a benchmark, and much has changed in NVIDIA's software since. Noise and power are easier to pin down. Reviewers describe the M5 Ultra as near-silent, and Apple lists 9 W at idle. Tom's Hardware measured a GB10 system at 37.5 dBA from 18 inches and about 35 W idle in January 2026, and later saw Founders Edition idle fall to 22–25 W after a software update.
Pick the Mac if your work starts and ends on your desk. Pick DGX Spark if your work starts on your desk and ends on an NVIDIA server.
Has the Mac AI software caught up?
Partly. Apple's MLX framework and the mlx-lm package now handle inference, LoRA and full fine-tuning, including on quantised models. Ollama 0.19 runs MLX as a preview on Macs with more than 32 GB, and LM Studio supports MLX models. The vllm-metal plugin, published under the official vLLM project, had its first official release (v0.28.0) on 22 September 2026, bringing continuous batching and an OpenAI-compatible server to the Mac.
The gaps are still real as of November 2026. There is no CUDA on macOS, so no TensorRT-LLM, no NCCL and no CUDA-only kernels. PyTorch's Mac backend is missing some operations, bitsandbytes runs only on the CPU, and Docker containers can't use the Mac GPU, so inference has to run natively on the host. The Mac multi-user serving tools are months old; the CUDA equivalents are years old. NVIDIA's 10-K describes "over 7.5 million developers" using CUDA and its other software tools, and most open-source AI code assumes that stack. Our analysis of CUDA vs Apple MLX goes further into what does and doesn't run.
When is DGX Spark still the better choice?
- You deploy to NVIDIA hardware. DGX Spark runs the same CUDA, PyTorch and container stack as DGX servers and cloud GPUs, so a model, a quantisation format such as NVFP4 or a serving setup tested on the desk behaves the same in production. This is the main reason teams buy it, and it's the trap we describe in the Mac Studio developer mistake.
- You fine-tune. NVIDIA publishes playbooks for PyTorch, NeMo, Unsloth and LLaMA Factory, and its own table shows QLoRA fine-tuning of Llama 3.3 70B at 760 tokens per second on one Spark. Our guide to LoRA and QLoRA on GB10 covers this.
- You serve several users. StorageReview measured gpt-oss-120b on a Dell GB10 system with vLLM at up to 671 tokens per second in total at batch 64. Measured multi-request scaling on the M5 Ultra has so far been modest.
- You want Linux and containers with GPU access. If your tooling is Docker-based, DGX OS works the way your servers do. Our Windows vs Linux local AI guide explains how GB10's Linux stack differs from RTX Spark on Windows.
Price is roughly level at the entry point: S$9,300 for a 4 TB DGX Spark at SourceIT against S$7,999 for the base M5 Ultra at Apple Singapore's list price, which has 96 GB rather than 128 GB. Past 128 GB, only the Mac goes further in a single box. For the broader market picture, see the Mac Studio vs GB10 local AI war.
Buying in Singapore
The NVIDIA DGX Spark 4 TB is S$9,300 at SourceIT (listed 8 Nov 2026), and the ASUS Ascent GX10 128 GB/1TB, built on the same GB10 chip, is S$10,350. More GB10 systems are in our DGX Spark collection. SourceIT lists the Mac Studio M5 Ultra 96 GB/1TB and M5 Ultra 256 GB/2TB as price on request; ask us for a quotation. Use Request for Quotation on the product page, call (65) 6978 3502, email Sales@sourceit.com.sg or use our contact page.
Frequently asked questions
Is a Mac Studio faster than DGX Spark for local AI? For generating text, published results put the M5 Ultra ahead, in line with its 1.2 TB/s of bandwidth against 273 GB/s. No benchmark has used the same engine on both machines, so the size of the gap isn't settled.
Can I run CUDA code on a Mac Studio? No. macOS has no CUDA, so CUDA-only tools such as TensorRT-LLM won't run. You would use MLX, llama.cpp or vllm-metal instead, and some code needs porting.
Can a Mac Studio fine-tune models? Yes, with mlx-lm, which supports LoRA and full fine-tuning. NVIDIA's tooling for fine-tuning is broader and better documented, and bitsandbytes-based workflows don't use the Mac GPU.
Which is better for serving a team? DGX Spark currently has the more mature multi-user serving stack through vLLM and SGLang on CUDA. Mac serving tools such as vllm-metal are new, and measured multi-request scaling on the M5 Ultra is limited so far.
Can I cluster either machine later? Both can be clustered. DGX Spark uses its 200 Gb/s ConnectX-7 ports, with NVIDIA software supporting up to four units. Mac Studios use RDMA over Thunderbolt 5, with direct cables and a four-machine full mesh at most.
