A week with RTX Spark: everything we’ve learned using Nvidia’s new laptops

Nvidia has officially launched its RTX Spark N1X processor, marking a significant shift in the laptop landscape. This new system on a chip is designed to compete directly with Apple’s M-series and Qualcomm’s Snapdragon X Elite. According to The Verge, the chip is already available in Windows laptops from six different manufacturers, signaling a major push into the Arm-based PC market.

We have collectively tested four of these new devices so far, and the initial impressions are compelling. The N1X is not just another incremental update. It combines high-performance Arm cores with Nvidia’s RTX Blackwell graphics. This architecture is specifically tuned to handle the heavy lifting required for modern local AI workloads.

The standout feature for AI enthusiasts is the unified memory architecture. Unlike traditional setups where CPU and GPU memory are separate, this design allows for massive data sharing. The flagship Microsoft Surface Laptop Ultra boasts an impressive 128GB of unified memory. This capacity is crucial for running sizable local AI models without constant swapping to slower storage.

Sean Hollister has been testing a 64GB version of the Asus laptop, providing a more accessible data point for the mid-range market. While 128GB is ideal for power users, 64GB still offers substantial headroom for most professional AI tasks. The key takeaway is that Nvidia is finally bringing desktop-class AI performance to portable form factors.

However, the ecosystem is still maturing. With six manufacturers involved, driver optimization and software compatibility vary. Some users may encounter bugs or performance inconsistencies that will likely be resolved in future updates. This fragmentation is a common hurdle for new hardware architectures, but it also suggests a competitive market that will drive rapid improvements.

The arrival of RTX Spark challenges the dominance of Intel and AMD in the high-performance laptop segment. It forces competitors to accelerate their own Arm-based strategies. For consumers, this means more choices and potentially better prices as the market stabilizes. The race is no longer just about raw speed, but about efficient AI acceleration.

What this means for you: If you rely on local AI models for your work, this hardware is a game changer. You can now run complex models on the go without needing a cloud subscription. Try this workflow: Use an AI assistant to convert your local documentation into a vector database, then query it using a local LLM on your new RTX Spark laptop. This keeps your data private while leveraging powerful on-device inference.

The technology is promising, but patience is required. As drivers mature and more software adapts to the Arm architecture, the experience will only improve. For now, early adopters are getting a glimpse of a future where powerful AI is truly portable.