DGX Spark vs Mac Studio & Halo: Benchmarks & Alternatives
NVIDIA’s DGX Spark entered the desktop AI market in 2025 at $3,999, positioning itself as a “desktop AI supercomputer”. It packs 128GB of unified memory and promises one petaflop of FP4 AI performance in a Mac Mini-sized chassis.
See the benchmark results on value and performance compared to alternatives:
Competitive analysis: DGX Spark vs. alternatives
GPT-OSS 120B performance
When comparing systems on the demanding GPT-OSS 120B model (MXFP4 format), performance differences became stark. 1 2
Key performance insights
- Prompt processing: DGX Spark and 3×RTX 3090 are nearly identical (1,723 vs 1,642 tokens/sec), with DGX Spark slightly ahead due to FP4 efficiency. The AMD Strix Halo lags significantly at 340 tokens/sec despite similar FP4 capabilities.
- Token generation: The 3×RTX 3090 setup dominates at 124 tokens/sec, more than 3× faster than DGX Spark’s 38.55 tokens/sec. This confirms that LPDDR5X memory bandwidth (273 GB/s) is the bottleneck compared to GDDR6X aggregate bandwidth.
- Memory capacity advantage: DGX Spark’s 128GB unified memory enables it to run models that would crash on 24GB GPUs. A single RTX 3090 cannot run 120B models without offloading to slower system RAM.
Source: LMSYS Org 3 , Substack 4
The chart demonstrates that:
- DGX Spark outperforms Mac Mini M4 Pro across all model sizes
- For smaller models (GPT-OSS 20B, LLaMA 3.1 8B), the gap is largest (~30% faster)
- For larger models (Gemma-3 27B), performance converges as both systems become memory-bound
- Both systems remain usable even with 27B parameter models
Price-performance analysis
Note: Prices are approximate as of January 2026
Raw performance benchmarks
llama.cpp results
Early benchmarks from llama.cpp developer Georgi Gerganov provides baseline performance metrics. The tests measured prompt processing (how quickly the model ingests input) and token generation (response speed):
Source: Hardware-Corner.net 5
The pattern is clear: DGX Spark excels at prompt processing (compute-bound) but struggles with token generation (memory-bound).
Ollama performance tests
Official Ollama benchmarks using firmware version 580.95.05 and Ollama v0.12.6 tested multiple models with standardized conditions:
Source: Ollama Blog 6
Note: OpenAI’s gpt-oss models tested by Ollama use the official MXFP4 format with BF16 in the attention layers, not the q8_0-quantized version
DGX Spark: Technical specifications
Source: NVIDIA7
When is DGX Spark better?
CUDA ecosystem access
The DGX Spark distinguishes itself in scenarios where software compatibility and specific workflow efficiencies outweigh raw token generation speed. For developers accustomed to Apple silicon, the transition to the Spark alleviates the friction of the “CUDA gap” because many industry-standard libraries and tutorials still presume a CUDA environment.8
The Spark provides native access to the NVIDIA ecosystem, including Docker containers and official playbooks, allowing users to run complex setups such as fine-tuning pipelines or agentic workflows that rely on the standard NVIDIA stack.
Desktop-to-datacenter workflow
This device effectively bridges the gap between local prototyping and datacenter deployment. Positioned as a “personal AI supercomputer,” it allows researchers to develop and test models on a desktop unit that shares the exact software architecture (drivers, CUDA toolkit, and management tools) as full-scale cloud clusters.9
This consistency addresses local environment compatibility issues when migrating workloads to large H100 deployments.
Furthermore, specific benchmarks highlight the system’s competence in fine-tuning and high-throughput batch processing; in testing, the system achieved approximately 924 tokens per second with Llama 3.1 8B (FP4) and 483 tokens per second with Qwen3 Coder 30B (FP8), demonstrating its utility for rigorous development tasks beyond simple chat inference.10
Hybrid setups with Mac Studio
Innovative hardware pairings also reveal specific advantages for the Spark. While it struggles with memory bandwidth for decoding compared to Apple hardware, its compute-heavy “prefill” performance is significantly stronger.
By networking a DGX Spark with a Mac Studio M3 Ultra, developers can leverage the Spark for prompt processing and the Mac for token generation. This hybrid “disaggregated” setup achieves a 2.8x overall speedup compared to running models on the Mac Studio alone.11
Alternatives to consider
AMD Strix Halo (Framework desktop) for budget & value
For budget-conscious users, the Framework Desktop with AMD Ryzen AI Max 385 (Strix Halo) offers the best price-to-performance ratio among unified memory systems. At $2,348, it costs roughly half of the DGX Spark while providing the same 128GB unified memory configuration and comparable memory bandwidth (~273 GB/s).12
Token generation performance is surprisingly competitive: 34.13 tok/s versus DGX Spark’s 38.55 tok/s on the 120B model. However, prompt processing reveals the gap, where DGX Spark’s Blackwell architecture dominates at 1,723 tok/s compared to Strix Halo’s 339.87 tok/s. This means Strix Halo ingests large contexts roughly 5× slower, though generation speed remains nearly identical once processing begins.
The trade-off is software maturity. Strix Halo relies on AMD’s ROCm stack instead of CUDA, which is improving rapidly but still lacks the ecosystem depth and pre-configured AI development environment that DGX Spark provides out of the box.
Mac Studio M3 Ultra for high-speed inference
If memory bandwidth and token generation speed are the primary metrics, the Mac Studio M3 Ultra remains a superior option. With 512GB of unified memory available at 819 GB/s, the Mac Studio offers roughly three times the bandwidth of the Spark’s 273 GB/s LPDDR5X configuration.13
This bandwidth advantage results in faster decoding speeds for large language models, making the Mac Studio highly effective for inference-heavy tasks where response generation time is critical.
Multi-GPU DIY builds for maximum raw performance
For maximum raw throughput regardless of complexity, a 3×RTX 3090 configuration delivers performance that no unified memory system can match. With 72GB of aggregate VRAM and ~936 GB/s total memory bandwidth, this setup achieves 124 tok/s on 120B models, more than 3× faster than DGX Spark’s 38.55 tok/s.14
The trade-offs are substantial. This approach requires significant technical expertise for setup and configuration, consumes 1,050W versus DGX Spark’s 210W, demands a larger physical footprint, and provides no out-of-the-box software stack. For users who prioritize turnkey convenience over raw performance, DGX Spark remains the easier path.
Limitations
Performance claims vs reality
The advertised “1 petaflop” figure relies on sparse FP4 precision, which initially raised questions about real-world applicability. We benchmarked FP4/INT4 quantization and found it retains 98% of model accuracy while delivering 2.7x throughput gains compared to BF16. However, the 2% drop in accuracy may be significant for precision-critical tasks such as code generation or mathematical reasoning, where minor errors compound quickly.
This performance gap can be jarring given the price point, particularly when older server CPUs or budget DIY GPU clusters can outperform the Spark in specific inference benchmarks due to the Spark’s memory bandwidth bottleneck.
Software and support concerns
Long-term viability and software friction also present significant hurdles. The DGX OS currently guarantees only two years of support, a short window for enterprise hardware, and the device has shown tendencies toward thermal throttling, which can force restarts during extended runs.15
Additionally, while the system runs CUDA, the underlying ARM64 architecture causes unexpected compatibility issues; developers may find that specific precompiled binaries for libraries like PyTorch are missing or difficult to configure compared to standard x86 environments.
Methodology
This analysis synthesizes benchmark data from multiple independent sources:
- Hardware-Corner.net 16 : Allan Witt’s llama.cpp benchmarks comparing DGX Spark, AMD Strix Halo, and multi-GPU systems.
- Ollama Official Blog 17 : Standardized performance tests using Ollama v0.12.6 with firmware 580.95.05.
- IntuitionLabs.ai 18 : Comprehensive review with SGLang and Ollama benchmarks across multiple platforms.
- Level1Techs Forum 19 : Wendell’s hands-on review focusing on the software ecosystem and practical use cases.
- Signal6520 : Developer perspective on CUDA ecosystem access and ARM64 compatibility challenges.
- EXO Labs21 : Hybrid DGX Spark + Mac Studio disaggregated inference testing with 2.8x speedup measurements.
- Jeff Geerling22 : Dell GB10 comparison, thermal throttling analysis, and DGX OS support limitations.
- Banandre23 : Independent performance analysis comparing marketed 1 PFLOP claims vs real-world 480 TFLOPS measurements.
- StorageReview24 : Fine-tuning and batch inference benchmarks (924 tok/s Llama 3.1 8B, 483 tok/s Qwen3 30B).
All benchmarks use publicly available models with consistent test conditions where possible.
💡Conclusion
Users should understand the DGX Spark not as a raw performance champion, but as an accessible, standardized development kit designed to lower the barrier to entry for serious AI research.
Its value lies in the polished “day one” experience; unlike DIY builds that require days of driver troubleshooting, the Spark arrives with a mature software ecosystem, extensive documentation, and pre-configured playbooks that allow immediate productivity.
It provides a stable, supported platform for researchers who need to validate workflows locally before scaling up, effectively serving as a functional slice of a datacenter that fits on a desk.
FAQs
Further reading
- Top 30 Cloud GPU Providers & Their GPUs
- GPU Software for AI: CUDA vs. ROCm
- Top 20+ AI Chip Makers: NVIDIA & Its Competitors
- Multi-GPU Benchmark: B200 vs H200 vs H100 vs MI300X
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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