The NVIDIA DGX Spark arrived as a game-changer for AI researchers and engineers, offering a standardized platform for training and inference workloads. But beneath the identical hardware, a silent competition unfolded: how would OEMs interpret NVIDIA’s thermal and airflow guidelines? The results, measured under real-world AI benchmarks, show that cooling design can dramatically alter performance—even when the core components stay the same.
Five systems—NVIDIA’s Founders Edition, Gigabyte, Dell, Acer, and ASUS—were put through their paces using OpenAI’s GPT-OSS-120B model across three workload scenarios: balanced, prefill-heavy, and decode-heavy. The goal wasn’t just to test raw performance but to expose how each manufacturer’s thermal engineering impacted temperatures, power efficiency, and even drive longevity.
The Thermal Divide
At first glance, the DGX Spark’s architecture appears uniform: the same GPUs, motherboard, and CPU across all models. Yet the way heat is managed reveals a stark divide. Acer’s implementation stands out as a thermal outlier, consistently running 10–15°C cooler than the others across CPUs, GPUs, NVMe drives, and network interfaces. During the most demanding prefill-heavy workload, its CPU peaked at 74.6°C—while Dell, Gigabyte, and the Founders Edition all hit 87–88°C. The GPU temperatures followed the same pattern: Acer’s stayed below 68°C, while the rest hovered around 80–82°C.
This isn’t just a minor difference. Sustained high temperatures can trigger thermal throttling, degrade component lifespan, and even affect memory performance during decode-heavy tasks. Acer’s approach suggests a more aggressive cooling strategy, possibly through enhanced airflow pathways or additional heatsink mass. The other OEMs, however, clustered tightly around NVIDIA’s reference design, indicating they prioritized consistency over innovation in thermal management.
Power vs. Performance
Interestingly, power consumption remained nearly identical across all systems, with peaks ranging from 69.3W (Acer) to 76.0W (Gigabyte). This uniformity confirms that the thermal variations aren’t due to differences in hardware efficiency but rather in how heat is dissipated. Gigabyte, despite running warmer in some areas, actually delivered one of the best cooling-to-power ratios, suggesting its design optimizes for both thermal headroom and energy use.
Storage temperatures also tell a story. Acer’s NVMe drive peaked at 51.8°C, while the others reached 58–63°C. Over time, this could translate to better drive longevity and more stable write speeds—critical for fine-tuning workloads where data is constantly being swapped or updated.
What It Means for Buyers
For end users, the takeaway is clear: cooling design matters. If thermal performance is a priority—whether for extended workloads or simply to avoid throttling—Acer’s implementation offers a measurable advantage. The other systems, however, perform nearly identically to NVIDIA’s Founders Edition, meaning buyers can trust they’re getting a well-vetted thermal solution, even if it’s not the coolest option.
Gigabyte emerges as a standout for balancing cooling and power efficiency, while ASUS sits in the middle ground. Dell and the Founders Edition, meanwhile, prove that NVIDIA’s reference design is reliable but not necessarily cutting-edge in thermal innovation.
As full reviews of each system roll out, expect deeper dives into workload performance and teardowns that reveal the mechanical secrets behind these thermal differences. For now, the data speaks for itself: in the world of AI hardware, even the smallest engineering choices can have a big impact.
