'These are outside the budget and needs of mainstream users, for now': I asked a tech expert who RTX Spark laptops are actually for

Nvidia and Microsoft just introduced a new laptop category: the RTX Spark platform. While the best laptops are designed for productivity or gaming, these new notebooks give creators and AI coders powerful machines that let them work on the go.

The promise of offline AI work will certainly appeal to some customers, but it also makes RTX Spark laptops a relatively niche offering. That was my main takeaway from testing the HP OmniBook Ultra 16. While impressive, it’s too overpowered for everyday work and relatively underpowered for gaming. Due to that, RTX Spark laptops might struggle to compete with their more popular direct competitor, high-end MacBook Pros.

To better understand who these machines are actually for, I reached out to Tom Mainelli, Group Vice President of Device & Consumer Research at IDC, for his take on where RTX Spark fits right now and where it might go from here.

Early adopters, developers, and creators

RTX Spark audience — (Image credit: Tom's Guide)

RTX Spark laptops target a narrow group: AI developers, content creators, and early adopters. These are premium notebooks built for people who already understand what the platform offers and why the price is what it is.

“These are outside the budget and needs of mainstream users, for now.”
Tom Mainelli, IDC

“These are outside the budget and needs of mainstream users, for now,” Mainelli said. At the same time, he sees them as a preview of where the PC industry is headed. The various configurations support different workloads, but the overall pitch is the same: a high-end Windows machine that can handle local AI work in a way previous laptops couldn’t.

Competing with MacBook Pro and gaming laptops

Nvidia RTX Spark — (Image credit: Tom's Guide)

Mainelli positions RTX Spark notebooks as competitors to similarly configured MacBook Pros. Both platforms use unified memory, which makes them better suited to running larger models locally than traditional Windows laptops with discrete GPUs.

The difference is that this is an Nvidia system-on-chip, so it brings native CUDA support to Windows on Arm — something Windows laptops haven’t had before. Windows has also added new setup features that make it easier to install and run local agent platforms such as OpenClaw, Hermes, and Perplexity Computer in a more secure, managed way.

Though Nvidia didn't market them directly as gaming laptops, it did show games running on RTX Spark systems. And given Nvidia’s gaming origins, it’s natural to wonder how these machines function as gaming systems. “Few vendors are marketing their RTX Spark systems as gaming systems, which typically have high-end discrete GPUs, but they appear to offer a solid gameplay experience,” Mainelli said.

"Few vendors are marketing their RTX Spark systems as gaming systems."
Tom Mainelli, IDC

My team and I noted that sentiment in our reviews of RTX Spark laptops, which tend to offer performance on par with RTX 5070 gaming laptops. Still, these aren’t — and shouldn’t be considered — gaming laptops. Gaming is simply an option, not a primary function.

Unified memory architecture

The unified memory design could change the value proposition. On the high-end 128GB configurations, you can allocate up to 96GB to the GPU. That’s far more than you can get with a discrete GPU in a laptop, and it means you can load sizable models into memory and potentially run more AI inference workloads locally.

“Because the unified memory architecture makes it possible to allocate more memory to the GPU than you can buy in a system with a discrete GPU, it could change the value proposition,” Mainelli said.

In theory, that could reduce what AI developers spend on cloud-based tokens. In practice, Mainelli said it’s still early. It requires a savvy user who’s willing to spend up front on the hardware and then experiment with local models to get the best results.

Cost is the biggest barrier

When I asked about the practical limitations that could slow adoption, Mainelli pointed to cost first. “Today, it is cost,” he said. “And the fact that to run some of the larger AI models locally, you need to buy higher-memory configurations.”

“Today, it is cost.”
Tom Mainelli, IDC

Some buyers may still hesitate about Windows on Arm, though he noted that Microsoft, Qualcomm, and the PC vendors have done a lot of work over the last few years to address most of those concerns.

Compatibility issues aside, I can see RTX Spark laptops potentially paying for themselves over time if they can speed up the work of AI developers and creators. Still, with the initial asking price being as “low” as $2,599 for a machine like the Microsoft Surface Laptop Ultra, even the target customer might have reservations.

More products are coming

Looking ahead 12 to 18 months, Mainelli expects more systems based on the RTX Spark platform. He doesn’t see Nvidia and its partners shifting focus away from the premium segment anytime soon, and commercial products could also appear. For now, though, these remain specialized machines aimed at people who already know why they want the combination of unified memory, CUDA, and local AI capability on Windows.

“I’m confident we’ll see additional products ship leveraging the RTX Spark platform in the future,” Mainelli said. “I expect NVIDIA and its partners to remain focused on the premium and potentially commercial segments in the nearer term.”

To be clear, RTX Spark isn’t trying to be the next mainstream Windows laptop. It’s a high-end option for a specific kind of buyer. Whether that audience grows beyond early adopters and developers will depend on how useful the local AI and memory advantages prove in everyday work — and whether that’s worth the cost of entry.