Benchmarking Local AI Hardware: NVIDIA DGX Spark vs. AMD Strix Halo for Cost-Optimized Workflows
A 3-day practical test compares speed, cost-per-token, and software performance between two 128GB AI desktop boxes, revealing unexpected community-driven optimizations.
Practical Summary
This report provides a concrete, side-by-side comparison of the NVIDIA DGX Spark and the AMD Strix Halo (Framework) desktop AI boxes. It moves beyond marketing specs to deliver reproducible benchmarks on raw speed (tokens/sec), operational cost (tokens per dollar), and software stack performance. Key practical findings include that while NVIDIA leads in raw throughput, AMD offers significantly better value for money. Crucially, community-maintained open-source drivers (Vulkan) can outperform the vendor's official software (ROCm) on the same hardware, a critical optimization detail. The hardware's large memory also fundamentally changes context window constraints for local model deployment.
Why It Matters
For developers and teams running local AI models, this comparison directly informs a high-stakes hardware and software investment decision. It provides evidence-based guidance on choosing between raw performance and cost efficiency. The discovery about driver performance highlights a key workflow optimization: leveraging community software can yield substantial gains without new hardware spend. Understanding the memory-to-context relationship helps in planning model deployment and avoiding unnecessary cloud costs for large-context applications.
Step 1: Understand the Core Trade-Off – Raw Speed vs. Cost Efficiency
The benchmark starts by establishing the fundamental performance difference. Using the same model (llama.cpp build), the NVIDIA DGX Spark achieved roughly 2x faster prompt processing and ~10% faster token generation compared to the AMD Strix Halo box. However, the critical cost metric tells a different story. When calculating tokens per dollar spent on hardware, the value shifts to AMD.
