Nvidia's RTX Spark partner systems are here, and spec-for-spec they've got all the ingredients for a hit: a potent CPU, blisteringly fast integrated graphics, and up to 128 GB of unified memory in a lightweight, ultra-premium package that's ready to run demanding models locally.

But with prices for these systems starting around $2,600 and going well north of $6,000 for the top spec trims, the one thing RTX Spark isn't is a good deal.

However, for the right person, by which we mostly mean AI enthusiasts, researchers, and/or creative professionals who need tons of video memory and don't want to get lost in Apple's walled garden or would prefer Nvidia's CUDA ecosystem over AMD's ROCm, the RTX Spark still has a lot to offer.

As such, we'll be largely focusing on the local agentic AI use cases Nvidia and Microsoft are pushing for these "builder-class" systems.

had the opportunity this week to put the OmniBook Ultra, HP's unique spin on the RTX Spark platform, through a series of AI benchmarks to see just how well it performs. And spoiler alert: you'll be hard pressed to find a faster Windows notebook for local AI.

But before diving into performance, let's take a closer look at the hardware itself.

No-compromise quality

Lifting the lid on the OmniBook Ultra 16, the first thing you'll notice is the gorgeous 2880x1800 pixel OLED touchscreen with a 16:10 aspect ratio.

Touch screens on professional notebooks can be a contentious topic. You either love them or hate them, but at the end of the day, if the idea of fingerprints obscuring your work gives you pause, no one is forcing you to use it, and we're happy to see it here.

The OmniBook Ultra's display is mounted to a surprisingly stiff hinge mechanism mounted to the top of the chassis. This has the benefit of keeping the hinge out of the way of the notebook's ample exhaust, which is kind of important because the 20-core N1X system-on-chip powering our review unit runs hot.

Speaking of keyboards, the OmniBook Ultra's digit fidgets are a bit of a throwback. Instead of the now-ubiquitous chiclet keys popularized by Apple in the early '10s, HP has gone for a more traditional notebook keyboard, which we found refreshingly easy to type on.

Keyboards of course are subjective so if there's any question on whether this one is right for you, it might be worth a trip to your local big box store to give one a good pecking before you commit.

Framing the keyboard are a pair of speaker grilles backed by four Cirrus Logic amplifiers which sound excellent for a Windows notebook, but can't quite match the clarity and bass of Apple's MacBook Pros.

With that said, we don't expect anyone will be disappointed by the OmniBook Ultra's speakers. Directly below the keyboard is a haptic trackpad that is as precise as it is enormous. No product in this price class should be shipping with a diving board mechanism in 2026, so it's nice to see HP didn't cheap out on us.

Moving to the sides of the notebook, you'll find a refreshingly comprehensive set of ports including:

Our only real critique here is that the OmniBook Ultra, like other laptops, lacks a full size SD card slot. MicroSD cards are convenient, but they're not widely used in professional environments and we'd have preferred to see a full size SD card reader or better yet a CFexpress card reader. However, it's rare if not impossible to see these on notebooks.

The chassis itself weighs in at 4.89 pounds and is milled from aluminum with a remarkably premium-feeling finish on the palm rest, lid, and bottom panels. HP has done a nice job building a notebook that feels as premium as it is powerful.

Into the belly of the beast

While the OmniBook Ultra is an objectively nice machine, the main attraction of any of the RTX Spark systems is going to be the N1X chip powering them.

The SoC features a MediaTek-derived CPU chiplet that's melded using advanced packaging and Nvidia's NVLink Fusion chip-to-chip interconnect to a Blackwell GPU.

The N1X comes in two flavors: An entry level version with 18 CPU cores and a GPU with 5,120 CUDAs. Meanwhile, the highest-end SKU — the one found in our review unit — features up to 20 CPU cores and a GPU with 6,144 CUDA cores. If you're wondering, that's exactly the same number as found in Nvidia's RTX 5070 just with access to a whole lot more memory.

We'll note that not all of those CPU cores are created equal. Like most Arm-based SoCs, the N1X contains a mix of big and little cores. Specifically, it has an equal complement of Arm X925 and Cortex A725 cores.

While all of this sounds impressive, it's worth noting that the chip really isn't all that new. The N1X is based heavily on the GB10, which launched in the Linux-based DGX Spark over a year ago. With that said, there have been some improvements.

At Hot Chips 2025, we learned the GB10 would support memory up to 9,400 MT/s. But, when the DGX Spark finally arrived that fall, it was only sporting 8,533 MT/s memory. The N1X rectifies this by boosting memory bandwidth to around 300 GB/s.

Higher memory bandwidth usually translates to faster token generation in AI inference and agentic workloads. So, while a fairly modest boost in bandwidth over the DGX Spark's 273 GB/s, it's still a meaningful one.

And that brings us to performance.

Your own personal AI assistant

There aren't many modern workloads that really need 128 GB of unified memory, but local AI certainly is one of them. While you can certainly get away with less, it opens the door to models with a decent shot at replacing something like Claude Code or Codex.

In fact, Microsoft believes that the future of Windows will be that of Copilot+ PCs that can run a mix of local and remote models with prompts routed between them based on things like complexity or privacy. Redmond is calling this technique hybrid intelligence.

But even with 128 GB of shared memory on board, you're still somewhat limited on what models can actually fit. That's because, unlike on Linux, which allocates memory dynamically to the DGX Spark's GPU as needed, Windows isn't quite asur testi smart.

The HP OmniBook Ultra arrived with a 50:50 split with 64 GB dedicated to the CPU and GPU respectively. This can be reconfigured as needed, but it's not exactly ideal. The good news is that, with the right software, you can get around this. Llama.cpp, one of the most popular local LLM inference frameworks, allows for overflowing into CPU memory as needed and, since the CPU and GPU share the same memory, the performance penalty isn't nearly as stark as it would when using a dedicated GPU.

Having said that, you have to fit a lot more than just the model's weights into memory. For agentic AI harnesses like Claude Code, models need to support large context windows with 128,000 tokens being a sweet spot these days. Depending on the model, that can chew up an awful lot of memory.

There is also the issue of the available models. While you could theoretically cram a 200 billion parameter model onto an RTX Spark using Llama.cpp, there aren't really many models that size.

Since Nvidia's RTX Spark platform ships with Windows, we conducted all of our tests using the v0.6.0 build of Llama.cpp across five models developed by Meta, Google, and Alibaba in sizes ranging from around 26 to 122 billion parameters.

Our first test simulates a chatbot-like environment tasking each system and model to process and generate a 256 token input and output.

There are two metrics worth paying attention to here: Time to first token (TTFT) measures the time in milliseconds required for the GPU to process the prompt and generally reflects the GPU's compute performance. Tokens per second (Tok/s) is a little more obvious and reflects how quickly the system can churn out tokens once the prompt has been processed. Also called decode, token generation is usually dependent on memory bandwidth, so faster memory tends to perform better here.

Across our test suite, we see performance scales neatly with model size and active parameter count.

Pitted against AMD's Strix Halo and Nvidia's earlier DGX Spark systems, which, again, aren't ideal comparison points since they're mini PCs with more robust cooling systems, the HP OmniBook Ultra performs quite well.

In Qwen3.6-35B-A3B — the fastest model we tested on any of the systems — the N1X's faster memory did give it an edge over the DGX Spark in token generation. The GB10 does appear to offer slightly higher compute performance, perhaps due to better thermals. Both Nvidia-based systems offer better performance than AMD's Strix Halo platform in our tests, though this shouldn't come as a surprise. On paper, Strix Halo offers less performance and memory bandwidth than either Nvidia chip and we've repeatedly shown this in testing over the past year. AMD's saving grace is that it has consistently offered better value.

While our single batch and context scaling benchmarks are useful for quantifying system performance across models, they aren't necessarily representative of how you're likely to use local models today.

If our numbers seem a little low compared to other benchmarks for the N1X and GB10, that's because our single batch results don't take advantage of prefill caching or speculative decoding.

If you're not familiar, prefill caching helps to speed up prompt processing time for information the model has already seen before. Speculative decoding meanwhile involves using a small draft model to predict the outputs of a larger one. In highly repetitive workloads, like agentic coding, speculative decoding speeds up token generation by 2-3x without any loss in quality.

AI agents, like code assistants, pass over the same prompts, code bases, and resources recursively, which makes them prime candidates for both techniques. In real world benchmarks, the overall line up remains unchanged to see high overall performance from these systems.

This test measures system performance when running an agentic coding workflow using Alibaba's Qwen Code (not to be confused with the Qwen-Coder family of models) harness and measures how quickly each system and model can solve a bin packing problem.

This addresses one of the more interesting phenomena that has cropped up around agentic AI. Faster token generation doesn't necessarily translate to a faster time to solve. Smarter but slower models can still be faster if they use fewer tokens to arrive at the correct answer.

And we see that here. Large models like Qwen3.5-122B-A10B ran slower on our test systems, generating tokens at roughly half the rate of the smaller 35B parameter model. Yet, the median time to solve was roughly 40 seconds faster on the larger model because it needed only a fraction of the tokens.

Having said that, given the extreme cost of memory right now, we believe the sweet spot for local AI code assistants is somewhere in the 20-60 billion parameter range, which is small enough to run on less expensive 64 GB versions of Nvidia's RTX Spark systems.

Teething troubles

As you might expect, any new platform is going to have a few growing pains at launch. It was certainly true when Intel introduced its performance and efficiency cores and when Qualcomm brought its Arm-based Snapdragon X Elite SoCs to the PC. Nvidia's RTX Spark platform is no different.

During our testing, we observed issues with thermal throttling or rather an apparent lack thereof. Under a constant AI inference load, we experienced occasional lock ups and, on one occasion, graphical artifacts as the GPU exceeded 90 degrees Celsius.

AI inference and training are some of the most resource-intensive workloads ever developed and to call them resource viruses wouldn't be inaccurate. So it's not surprising that a sustained inference load over the course of several hours might overwhelm the thin-and-light's thermal management system.

Under real world conditions we don't expect this will be an issue. Even a few seconds between prompts was enough for the notebook to remain stable and artifact free. With that said, if you were hoping to fine tune LLMs, you might want to look at Nvidia's DGX Spark or any one of the other GB10-based AI workstations from HP, Dell, Lenovo, and others. Their larger chassis and thermal management systems make them far better candidates for the often hours-long process of fine tuning models. Software support for PyTorch also happens to be a bit better on Linux than on Windows.

Beyond a cooling system that's perhaps a bit optimistic for what is supposed to be a 140 watt SoC, the only glitch we ran into was the Cirrus Logic audio outputs disabling themselves out of the blue. Thankfully all it took to get them running again was to uninstall them in device manager and reboot.

Summing up

The OmniBook Ultra 16 and the rest of the RTX Spark lineup occupy a rather niche segment in 2026. With memory prices at an all time high, the system spans the gamut from ultra-premium thin-and-light in its base config and mobile workstation at the upper end of the spectrum.

The OmniBook Ultra 16 is an objectively strong performer and an extremely well built one at that.

But at around $3,200 for the base model OmniBook Ultra 16 with the N1X and $6,229 as tested here, none of these systems are cheap. Had Nvidia and its partners launched these notebooks a year ago at between $2,000 and $4,000, the story might have been different.

Unless you've drunk the AI Kool-Aid and are buying an RTX Spark system for local AI development or happen to have a particularly VRAM hungry workload that makes you money, these systems are going to be a tough sell for the same reason it's hard to recommend workstation components to the average consumer. Most folks can't take full advantage of them and their money is usually better spent elsewhere. And if you can and you're willing to give up a little performance, AMD's Strix and Gorgon Halo-based notebooks and mini PCs are definitely worth cross shopping.

As memory prices normalize, we do expect Nvidia's next-generation of RTX Spark systems to be both faster and more attractively priced. Unfortunately, we will wait a while for that to happen. The memory fabs expect DRAM supplies to remain constrained through at least 2028.

We've got a whole lot more testing planned for the RTX Spark and HP's OmniBook Ultra, including a comprehensive set of CPU and gaming benchmarks, along with a deeper dive into agentic AI performance as we look to answer the question whether these systems can actually save you money. So far that argument isn't looking good. ®