Microsoft N1Xes Intel in favor of Nvidia's shiny new SoCs in Surface Laptop Ultra

Look out, Intel! Microsoft released its first Surface notebook powered by Nvidia's RTX Spark system-on-chip (SoC) during an event in San Francisco on Wednesday.

The Surface Laptop Ultra is one of several new Copilot+ PCs powered by the N1X chip that Microsoft says is aimed at "builders," which are available for preorder starting today and expected to begin shipping next week.

The machine is arguably the vehicle for a slew of new AI features including hybrid compute functionality that dynamically routes workloads to on-device or cloud models depending on factors like connectivity, privacy, cost, and complexity.

The aluminum clad system largely keeps with Microsoft's existing design ethos.

It features a high-res, 15-inch mini-LED display capable of reaching a peak brightness of 2,000 nits in Microsoft's signature 3:2 aspect ratio, a standard ten-keyless keyboard layout, haptic trackpad, and a slew of ports including one you won't find on a MacBook Pro.

Connectivity includes three USB-C ports, including one equipped with magnets for MagSafe-like connectivity, full-size HDMI, an SD card reader, and a solitary USB-A port because after more than a decade still can't shake the aging connector.

But arguably the notebook's most interesting feature is the inclusion of Nvidia's RTX Spark N1X SoC.

As we reported earlier this year, these aren't exactly new chips.

The N1X is based heavily on Nvidia's now year-old GB10 platform, with a few CPU and GPU cores fused off on lower end models to meet pricing.

This tells us that the Surface Laptop Ultra should ship with either an 18- or 20-core CPU designed by MediaTek and Arm and an Nvidia Blackwell-based RTX GPU with between 5,120 and 6,144 cores.

In terms of memory, Microsoft says the notebook can be equipped with up to 128 GB of memory, enough to serve large language models up to 284 billion parameters in size, if you can live with just 1.6 bits of precision.

As we've previously covered, the GPU is a pretty potent one with a peak theoretical throughput of 1 petaFLOPS at 4-bit precision, not that you're likely ever to see that in many real world workloads.

Nvidia's marketing team is leaning on support for 2:4 sparsity to hit that target, so it can theoretically achieve that figure.

Unfortunately, almost none of the AI inference workloads anyone cares about these days actually take advantage of the functionality.