Hands-On: Comparing AI Powerhouses – Nvidia DGX Spark vs. AMD Strix Halo and HP Z2 Mini G1a

Most current Generative AI (GenAI) models are trained and operated within massive data center clusters. However, the ability to build, test, and prototype AI locally remains highly relevant. Until recently, this required expensive, multi-GPU workstations costing tens of thousands of dollars—until Nvidia launched its GB10-based DGX Spark in October. While not matching the power of data center giants, the Spark, with 128 GB of video memory, is essentially an on-demand AI lab in a compact package capable of handling nearly any AI workload.

Variants of AI Workstations: Power, Cost, and Accessibility

The Spark isn't the only option. AMD and Apple offer systems with large unified memory shared between CPU and GPU, making them popular among AI enthusiasts.

AMD's Strix Halo and HP's Z2 Mini G1a

Strix Halo (Ryzen AI Max+ 395 APU) is particularly interesting due to its affordability—costing between three-quarters and half of the Spark—and its software stack built on ROCm and HIP, easing the transition from desktop to data center.

HP's Z2 Mini G1a offers advantages in size and serviceability. It uses soldered LPDDR5x memory and includes two user-upgradable PCIe 4.0 M.2 SSDs, whereas the Spark's SSD is less accessible and more appliance-like. The G1a's larger chassis allows for better cooling and serviceability, with easy access via a single button press.

System Overview and Connectivity

| Feature | HP Z2 Mini G1a | Nvidia Spark (GB10) | |---------|----------------|---------------------| | Size | Larger, with integrated PSU | Compact, all-metal chassis, external power brick | | Cooling | Larger, effective cooling solution | Heat sink chassis, integral cooling | | I/O Ports | 2x 10 Gbps USB, 4x USB 2.0, Thunderbolt 4, DisplayPorts, audio jack | 4x USB-C (including power delivery), HDMI, 10 GbE RJ45, QSFP cages (200 Gbps network) | | Expandability | User-accessible SSDs, serviceable design | SSD swap via magnetic plate, appliance-like |

The Spark emphasizes high-speed networking for multi-node clusters—ideal for data center environments—while the G1a prioritizes simplicity and connectivity, suitable for individual or small-scale AI work.

Performance: Speeds and Feeds

| System | CPU | GPU | Memory | Approximate MSRP | |---------|-------|-----|--------|------------------| | HP Z2 Mini G1a | 16 Zen 5 cores @ 5.1GHz | Radeon 8060S | 128GB LPDDR5x | ~$2,950 | | Grace Blackwell (GB10) | Arm Cortex A925 & A725 cores | AMD Radeon Pro W6800S | 128GB LPDDR5x | ~$3,999 |

While the Spark is more expensive, it is aimed at high-end AI workloads. Notably, neither system is the cheapest for their silicon; OEM versions with similar specs start around $3,000 for the Spark and $2,000 for Strix Halo.

CPU Details

• Strix Halo features 16 Zen 5 cores (up to 5.1GHz) bonded via advanced packaging, with a core-complex design typical of desktop CPUs.
• G1a employs the AMD Pro variant, adding security and management features suited for enterprise deployment.
• Spark's GB10 chip combines 10 performance cores and 10 efficiency cores, optimized for AI and HPC tasks.

Performance Benchmarks

In various workloads, AMD's Zen 5 cores outperform the Spark's ARM CPU by 10-15%. However, in high-performance Linpack benchmarks, the G1a shines—with over twice the double-precision GFLOPS compared to Spark.

Generative AI Capabilities

Peak and Sustained AI Compute

• Nvidia Spark claims up to 1 petaFLOPS of AI compute, but real-world performance is closer to 250-500 teraFLOPS due to data sparsity requirements.
• Strix Halo's GPU can achieve about 56 teraFLOPS BF16 peak performance, with tested max around 46 teraFLOPS—still solid, but less than Nvidia’s offering.

Language Model Inference

In single-batch testing with Llama.cpp, the AMD system slightly outpaces the Spark in token generation speed, attributable mainly to memory bandwidth (~273 GB/s vs. ~256 GB/s). However, the Spark's GPU excels in reducing time-to-first token—critical for interactive applications—especially as prompt length increases.

Multi-Batch and Fine-Tuning

Batch processing with large inputs favors the Spark due to its faster GPU, delivering higher throughput at scale. For fine-tuning, both are capable with up to 128 GB memory; the Spark generally completes training tasks faster, making it more appealing for intensive model adaptation.

Image and Video Generation

For compute-heavy image generation, such as running models like Stable Diffusion in ComfyUI, the Spark's GPU offers approximately 2.5x performance over the Strix Halo, thanks to its higher BF16 throughput (~120-125 TFLOPS).

AI NPU and Disaggregated Inference

AMD's Strix Halo includes the XDNA 2 NPU, capable of 50 TOPS. Software support is limited but growing—applications like Lemonade Server showcase potential for running lighter AI workloads, like background blurring or simple language tasks, on the NPU alone. Notably, generated images with Stable Diffusion using the NPU completed faster in Amuse, a beginner-friendly app, than on the GPU.

Software Ecosystem and Compatibility

• Nvidia's CUDA ecosystem remains dominant, with extensive library and tool support, making the Spark a plug-and-play for most AI frameworks.
• AMD's ROCm has made significant advances but still lags behind. Many libraries require building from source or using forks—an extra hurdle for developers.
• Compatibility challenges are primarily due to architecture differences, especially with AMD's RDNA 3.5 based Strix Halo lacking support for some low-precision data types favored in AI workloads. AMD's upcoming RDNA 4 architecture aims to address this.

Gaming and Practical Usage

Surprisingly, both systems can run gaming titles like Crysis Remastered at 1440p medium settings—although the Spark's Arm CPU makes setup trickier. While gaming is a secondary feature, performance here underscores their flexible hardware design.

Summing Up: Which System Is Right for You?

• If your focus is generative AI, model fine-tuning, and large-scale AI prototyping, the Nvidia DGX Spark offers unmatched performance and mature software support—ideal as an "AI lab in a box."
• For single-user inference, development, or budget-conscious setups, the AMD Strix Halo and HP Z2 Mini G1a provide competent, Windows- and Linux-compatible platforms without the steep price or complexity.
• Software compatibility remains a key consideration—Nvidia's CUDA ecosystem is more proven at scale, while AMD's ROCm continues to close the gap.

Final Thoughts

Choosing between these systems depends on your specific AI workload, budget, and preference for ease of use. The Spark shines in performance-heavy tasks and enterprise settings, while AMD's offerings excel in flexibility and cost-effectiveness for smaller-scale or general-purpose workloads.

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Interested in deploying AI at home or in the lab? The landscape is evolving, and both options present compelling cases. Happy AI building!