Nvidia DGX Spark vs. AMD Strix Halo: A 3-Day Benchmarked Comparison for Cost-Effective Local AI
A detailed, real-world benchmark comparing Nvidia's faster DGX Spark to AMD's cheaper Strix Halo platform, revealing key trade-offs in raw speed, cost-per-token, and surprising driver performance for local AI workloads.
Practical Summary
This comparison provides actionable data for choosing between two high-memory AI platforms. Nvidia wins on raw speed, but AMD delivers significantly better tokens-per-dollar, especially in budget builds. A key finding is that a community Vulkan driver outperforms AMD's official ROCm software on this specific hardware, highlighting the importance of driver testing. Both platforms remove memory as a constraint for very large models.
Why It Matters
For teams evaluating hardware for local AI inference, this analysis offers evidence-backed benchmarks to make a cost-optimization decision. It moves beyond marketing claims to show actual performance per dollar, helping justify capital expenditure or choose a platform that aligns with budget and performance priorities. The driver performance insight is particularly valuable for optimizing existing hardware.
Step 1: Understand the Core Trade-Off - Speed vs. Cost
The benchmark compares two 128GB unified memory systems: the Nvidia DGX Spark ($4,699) and an AMD Strix Halo-based Framework PC ($3,449). The fundamental decision is between Nvidia's raw processing speed and AMD's better value per token.
Step 2: Analyze the Cost-Per-Token Efficiency
The more critical metric for many use cases is efficiency. Tokens generated per $1,000 spent on hardware: Nvidia Spark yields ~12.5, the tested Framework Strix yields ~15.5, and the cheapest 128GB Strix box yields ~27.3. This demonstrates AMD's advantage becomes more pronounced with lower hardware costs.
