Two devices have emerged as the go-to options for businesses deploying local LLMs on-premise: the NVIDIA DGX Spark with its GB10 Superchip, and the Apple Mac Studio M3 Ultra. They sit in a similar price bracket, yet they are optimised for fundamentally different tasks. This article breaks down the architecture differences, reported performance numbers, and the practical question every SMB faces: which one do you actually buy?
Inside the NVIDIA DGX Spark
NVIDIA markets the DGX Spark as a "personal AI supercomputer." At its core is the GB10 Superchip, a system-on-chip manufactured on TSMC's 3nm process. It pairs a 20-core ARM CPU (10× Cortex X925 performance cores + 10× Cortex A725 efficiency cores) with a Blackwell GPU, sharing 128 GB of LPDDR5X memory across a 256-bit bus. NVIDIA claims up to one petaFLOP of FP4 inference performance from this configuration.
The footprint is compact — roughly 15 × 15 × 5 cm — making it easily desk-deployable without a dedicated server room. Current list price after a 2026 price adjustment: approximately $4,699 USD.
Where the DGX Spark excels: compute-bound tasks
The Blackwell architecture is purpose-built for compute-heavy AI workloads. Practitioners in the llama.cpp open-source community (GitHub Discussion #16578) report the following token generation speeds:
- 120B parameter model (MXFP4 format): approximately 25–35 tok/s
- 30B model Q8_0 (e.g. Qwen3-Coder-30B): approximately 20–38 tok/s
- Prefill speed, 120B model at 2,048 tokens: over 1,000 tok/s
The prefill performance is particularly noteworthy for RAG pipelines that load long documents into context before generating a response. For batch processing — ingesting contracts, technical manuals, or customer correspondence at volume — the DGX Spark's Blackwell compute delivers real throughput advantages.
The bandwidth ceiling: where DGX Spark struggles
At 273 GB/s of memory bandwidth, the DGX Spark hits a structural ceiling when running large models in unquantised formats. Community benchmarks put Llama 3.3 70B in BF16 (full precision) at only around 2–3 tok/s on the DGX Spark — too slow for fluid interactive use. The device compensates with MXFP4 and other quantised formats, but not all models are available in NVIDIA's native quantisation formats yet.
Inside the Mac Studio M3 Ultra
Apple's Mac Studio M3 Ultra brings approximately three to four times more memory bandwidth than the DGX Spark — and that difference is decisive for token generation on large models. Configurations scale up to 512 GB of unified memory, allowing the entire Llama 3.3 70B or larger models to sit in RAM at high precision.
Since Ollama added native MLX support (May 2025), quantised models run very efficiently on Apple Silicon. Community reports put Llama 70B in 4-bit quantisation at roughly 15–25 tok/s on a 192 GB M3 Ultra — measurably faster than BF16 on the DGX Spark for interactive conversation.
The macOS software ecosystem is also a practical advantage: Ollama, Open WebUI, LM Studio, and Continue.dev all run natively without Linux administration experience.
Performance at a glance
| Feature | DGX Spark GB10 | Mac Studio M3 Ultra |
|---|---|---|
| Memory | 128 GB LPDDR5X | up to 512 GB unified |
| Memory bandwidth | 273 GB/s | ~800+ GB/s |
| FP4 AI compute | 1 petaFLOP | no native FP4 GPU |
| Llama 70B BF16 | ~2–3 tok/s (reported) | — |
| Llama 70B 4-bit | ~10–20 tok/s (reported) | ~15–25 tok/s (reported) |
| LoRA fine-tuning | ✅ Unsloth, CUDA | limited |
| List price (approx.) | $4,699 USD | from $3,999 USD |
All tok/s figures are based on community-reported benchmarks, not Freshlab measurements.
Which workload fits which hardware?
Choose the DGX Spark if you need:
On-device fine-tuning. Using Unsloth alongside tools like LLAMA Factory or NeMo, the DGX Spark supports LoRA fine-tuning of 70B models directly — no cloud upload, no API key, no third-party data processor. For businesses handling sensitive customer data or proprietary knowledge bases, this is a significant compliance advantage.
High-volume document processing. Law firms, consultancies, and manufacturing companies that batch-process large numbers of long documents benefit from the DGX Spark's prefill performance (1,000+ tok/s at 2,048 input tokens reported). Automating contract review or technical specification parsing becomes meaningfully faster.
CUDA ecosystem and scalability. vLLM, SGLang, TensorRT-LLM — the professional inference stack runs on CUDA. Teams planning to scale beyond a single device benefit from NVIDIA's tooling. EXO Labs has demonstrated that combining two DGX Spark units with a Mac Studio M3 Ultra achieves approximately a 2.8× benchmark improvement over the Mac Studio alone — a practical path for growing teams.
Choose the Mac Studio M3 Ultra if you need:
Fluid, interactive AI assistants. For internal chat tools, coding assistants, or customer-facing knowledge bases, token generation speed directly shapes the user experience. The Mac Studio's bandwidth advantage translates into noticeably more responsive conversations.
Larger models at higher precision. With 192–512 GB of memory, you can run 70B or even larger models without quantisation-induced quality trade-offs. For use cases where model output quality is non-negotiable — legal document drafting, medical information summarisation — this matters.
Low operational complexity. macOS + Ollama is an order of magnitude simpler to set up than CUDA + Linux server tooling. For SMBs without a dedicated IT team, that operational simplicity has real value.
GDPR and data sovereignty: where both devices win
On data protection, both options offer the same fundamental advantage over cloud APIs: data stays on-device. Prompts, documents, and model outputs never leave the hardware. There is no US-based data processor involved, no Standard Contractual Clause risk, and no dependency on a vendor's terms of service changing.
Under the EU AI Act, as we read the current obligations, businesses that deploy local LLMs fall into the "deployer" category — subject to documentation and oversight requirements under Article 26, but not to the more demanding transparency obligations imposed on providers of general-purpose AI models. Running locally simplifies that compliance position substantially.
For pan-European SMBs subject to both GDPR and the EU AI Act: the choice between DGX Spark and Mac Studio does not change your legal obligations, but both keep you in control of where your data lives. More background: local AI and data sovereignty.
Our practical recommendation
For most SMBs deploying their first local AI workstation, the Mac Studio M3 Ultra is the more pragmatic starting point: a broader software ecosystem, less operational overhead, and better interactive performance for the everyday assistant use cases that deliver immediate value.
The DGX Spark becomes compelling once requirements around fine-tuning, high-volume batch automation, or future cluster scaling emerge. The two architectures are complementary — hybrid deployments combining both are already being actively explored in the community.
Unsure which configuration fits your processes? We advise SMBs on hardware selection, setup, and GDPR-compliant integration. Start a conversation with us — or explore our pilot project framework to scope out a concrete deployment plan.