实测微软Surface Laptop Ultra中的RTX Spark Superchip

Nvidia CEO Jensen Huang announced the RTX Spark platform with the grand ambition to “reinvent the PC” back at Computex 2026. By pairing the GB10 Superchip with a version of Windows on Arm tailored for the secure and contained execution of local AI agents, Nvidia and its partners at Microsoft, Dell, HP, MSI, Lenovo, and Asus are all hoping to pave the way to a future where the PC is an active, autonomous companion for work and creation.

The hardware foundation for that vision of the future is now in place. Last week, we flew out to San Francisco to get all the details on the RTX Spark platform, and we returned home with Microsoft’s Surface Laptop Ultra in top-end trim to put it all to the test.

This system has the full RTX Spark Superchip with 20 CPU cores and 6144 CUDA cores alongside 128GB of LPDDR5X-9400 RAM, and I’ve been putting it through its paces over the past week to see just what it can do.

The embargoes for the RTX Spark launch are a bit peculiar. Today, we’ll be sharing our broad experience with the Spark platform, as well as competitive performance evaluations, but not any system-specific results such as battery life, build or display quality, or other details of that nature. We have other RTX Spark laptops in the Tom’s Hardware labs, and detailed reviews of those systems will be coming soon.

No, this approach doesn’t make a lot of sense when the experience of a mobile platform is intimately and inextricably tied to the specific implementation of that chip in a particular laptop design. And we know that battery life is the first and foremost concern of many interested in this platform. But again, all that information is coming soon.

For this first round of testing, we compared the Microsoft Surface Laptop Ultra with RTX Spark against two systems. The first is a 14" MacBook Pro with Apple's highest-end M5 Max SoC inside. That Mac has a 40-core GPU and an 18-core CPU, as well as 128 GB of LPDDR5X RAM. We don't have a mobile Ryzen AI Max+ 395 system in the labs right now, but we do have AMD's compact Ryzen AI Halo as a source of fresh data with the latest ROCm stack and llama.cpp builds.

Local AI performance

Judging local AI performance is a constantly moving target, and this review is no different. Our past testing has exercised the prompt processing and token generation phases of AI inference separately, with the understanding that a system that's fast at both those phases provides the best experience.

But in an agentic workflow, that benchmarking technique doesn't capture the total time required to complete a task across all the inference turns needed to reach a particular goal. To capture that entire end-to-end time, the folks at Artificial Analysis have developed a new benchmark, AgentPerf-local, that precisely replays inference traces across several agentic AI tasks. The value of this approach should make immediate sense: the faster an AI agent finishes a job, the more productive a system can be.

So instead of presenting raw time-to-first-token and tokens-per-second figures, as we have in the past, we’re sharing time-to-completion values as we would for more traditional CPU and content creation benchmarks.

Again, local AI benchmarking is a continuous work in progress, so let us know what you think of this approach, and we’ll take that feedback into account as we continue to develop our methods.

We ran AgentPerf-local with two compatible models: the dense Qwen 3.8-27B, which is popular as one of the most intelligent local models available for its size, and the Qwen 3.6 35B A3B mixture-of-experts model, which is less intelligent but easier for a wide range of systems to run quickly. We used the latest build of llama.cpp as our inference backend and built it from source on each test system to take advantage of CUDA, Metal, or ROCm acceleration, as appropriate.

Let’s look at Qwen 3.8 27B’s behavior first. We used Unsloth’s Q4_K_M quantization of the model. We ran it with DFlash2 speculative decoding on the RTX Spark and MacBook Pro, and with Qwen 3.8 27B’s built-in MTP on the Ryzen AI Halo for the best performance.

As we found in our benchmarking of Apple's Mac Studio with the M4 Max SoC, memory bandwidth is only one constraint for local AI performance. Despite its memory bandwidth advantage on paper, the M5 Max MacBook Pro still ends up taking about 25% longer to finish the AgentPerf benchmark with Qwen 3.8 27B than the Surface Laptop Ultra with RTX Spark.

Part of that may be down to software maturity, but it's also worth remembering that our Surface Laptop Ultra is a 15" system with a much larger 140W power adapter versus the 14" MacBook Pro's 96W charger.

The 16" M5 Max MacBook Pro also comes with a 140W adapter, and we'd be curious to see whether the extra power and higher thermal headroom afforded by that system’s larger chassis help close the gap here. But in any event, the RTX Spark platform is the fastest overall at this task today.

Meanwhile, AMD's nearly two-year-old Ryzen AI Max+ 395 just can't keep up with either of these newer systems in this demanding test. It needs about twice as long to finish our agentic tests with Qwen 3.8 27B as either the RTX Spark or the MacBook Pro do, at a whopping 40 minutes.

The fact that the Ryzen AI Halo is a mini desktop form factor rather than a notebook doesn’t much matter to these results, as any power and thermal headroom that design might afford can’t possibly overcome the performance gap here.

Next up, we have the lighter-weight Qwen 3.6 35B A3B. We ran Unsloth’s Q4_K_M quantization of this model with MTP enabled.

The Surface Laptop Ultra ends up completing this test in just eight minutes, or about 25% faster than the 14” MacBook Pro’s 10 minutes. This test is also much friendlier to the Ryzen AI Halo, which finished the benchmark in a more reasonable 13 minutes. But that just emphasizes that you have to trade away model intelligence to get a reasonable level of performance out of Strix Halo.

AMD has recently gone on a publicity blitz to highlight the relatively low cost of entry for Strix Halo systems versus DGX Spark desktops and now RTX Spark laptops. But if time is money, and getting more intelligent tokens out of your local AI system faster means more potentially profitable agentic work gets done, then a more expensive system upfront will be able to do more of that work and potentially generate more value over time.

Gaming performance: first impressions

Despite its RTX branding and the emphasis Nvidia places on its 6144-CUDA-core GPU (or 48 SMs), the RTX Spark platform faces some tough challenges for gaming. Most games available today are not going to be natively compiled for its Arm CPU, and even for those that might be, the maximum 300 GB/s of shared memory bandwidth the platform offers is well below that of even the RTX 5060 Mobile's dedicated 384 GB/s feeding 26 SMs.

The entire GB10 SoC is competing for a relatively small amount of memory bandwidth, and because graphics is generally a memory-bandwidth-bound workload, that's not a great start for feeding all of those SMs in games.

I started my gaming explorations with Gears of War: E-Day. In this exceptionally CPU-heavy game, it's clear that the baseline performance of the Grace CPU cores and the added overhead of the Prism x86 emulator are doing this title no favors.

GPU utilization only reaches the 85% mark on average at a near-1080p (1908x1272) render resolution with the High preset and 2X framegen, and we saw at least one major hang during a map transition that was less than confidence-inspiring for an immersive experience as we progress further into the game.

Because of its CPU dependence, Gears is probably a worst-case scenario for this platform, but the fact remains that it's not a great experience, and turning down settings or lowering upscaling input resolutions just exacerbates the CPU-boundedness of this title. If any game could use a native Arm build, this is the one.

Control Resonant is significantly less dependent on the CPU, but that just means that the other potential gaming shortcomings of the RTX Spark Superchip seem to come into play.

After struggling a bit to achieve a solid frame rate and input latency at the Surface Laptop Ultra's native 3270x2180 output resolution even with DLSS upscaling, I found that running the game in windowed mode at 2560x1440 with DLSS Performance upscaling, the High preset, and MFG 3X to keep frame rates near the Surface Laptop Ultra's 120Hz refresh rate provided better results. (This system's tall 3:2 aspect ratio means that 16:9 fullscreen games don't cleanly scale up.)

But even then, input latency was above what I would consider acceptable on our standard GPU test rig, and enabling ray tracing at all dipped frame rates and spiked input latency even further, so I had to stick to raster-only rendering for the best results.

Overall, my early RTX Spark gaming verdict is that you can do it, and you can have a fine enough time, but it is not the primary focus of this platform. Gaming performance with the relatively limited memory bandwidth of the RTX Spark Superchip, combined with the system's CPU performance and the overhead of Prism emulation, mean that you'll be searching for a narrow range of settings where baseline frame rates, upscaling, image quality, and the smoothness boost of frame generation still yield acceptable input latency.

When I first approached gaming on this platform, I had expected that DLSS upscaling would be a required part of the experience, but in practice, you really need to lean on DLSS Frame Generation, as well, whether that's for added smoothness (as in Gears of War) or for both smoothness and the benefits of Nvidia Reflex due to developer choices behind the scenes (as in Control Resonant).

Even with Reflex, though, the Grace CPU generally seems to deliver input latencies significantly higher than I'm used to on our Ryzen 7 9800X3D test rig, and it's hard to get them much lower in these AAA titles by tweaking settings.

If you demand the lowest input latency and the highest performance with cutting-edge games, especially if you plan to enable RT, a separate gaming laptop with a dedicated RTX 50-series GPU will still provide a superior experience. And if you just don’t care about local AI at all, that advice applies even more strongly.

Bottom line

Nvidia's RTX Spark is an ambitious new PC platform for the traditional workloads of today and the potential agentic AI work of tomorrow. How often do we see an entirely new player try to elbow into the crowded and competitive PC space, especially one with a sweeping vision for how the PC will be used in the years to come? An incremental move this is not.

Our general performance tests and new agentic AI tests show that despite providing merely solid CPU performance and relatively low bandwidth for a unified memory architecture on paper, the Blackwell GPU on board GB10 has enough AI horsepower in practice to let the RTX Spark platform come out ahead of Apple's latest top-end M5 Max and AMD's Ryzen AI Max+ 395, aka Strix Halo, SoCs.

Whether you choose the highly intelligent dense Qwen 3.8 27B model and trade raw speed for intelligence, or make the opposite trade with the lighter and faster Qwen 3.6 35B A3B, the RTX Spark comes out on top. That bodes well for pairing popular agents like Hermes with intelligent local models on this platform.

But Nvidia's mission to reinvent the PC isn't a total grand slam in this first iteration.

First, this platform won't replace a dedicated gaming laptop. Even for those who only game on the side and have more forgiving standards for frame rates and input latency, the RTX Spark's gaming performance may not measure up to expectations.

When you put the overhead of Prism x86 emulation on top of the merely adequate performance of the Grace CPU, it’s clear that the Spark platform will have to work hard to keep up in the vast range of games that don’t have an Arm port.

The CPU-heavy Gears of War: E-Day is particularly unkind to the Prism-on-Grace stack, but even when a title like Control Resonant doesn't lean so heavily on the CPU, the limited and shared memory bandwidth of the GB10 SoC still poses an obstacle to cranking settings to the max despite the large amount of theoretical compute horsepower on tap.

We're also still waiting for Windows to fully catch up to Nvidia's ambitions for secure, manageable local agents. Our laptop expert Andrew Freedman and I couldn't test Microsoft's secure agentic AI promises within Windows 11, as the supporting features that Microsoft and Nvidia have promised to that end just aren't ready yet.

Still, for a first-generation mobile platform that joins complex silicon IP from multiple design teams onto one chip and relies on several OEM partners to bring it to life in highly integrated notebook form factors, the maturity of the RTX Spark platform is an impressive achievement. (Take it from someone who has seen a couple of rough product launches from the inside.)

Over the past week, I've been using the RTX Spark-powered Surface Laptop Ultra as I would any other Windows laptop, and aside from a couple of tiny paper cuts, I have mostly just gotten my work done as I would with any other computer. And that’s a great — albeit a very, very expensive — place to be as the local agentic AI future coalesces.

Perhaps the biggest challenge for the RTX Spark platform is that the eye-watering price tags for the range of systems available at launch still buy a lot of cloud tokens from OpenAI and Anthropic. Emphasis on a lot. The Surface Laptop Ultra we tested lists for $5899 with only 1TB of storage, and lead times for that system are already out to late December.

If you're not as concerned about keeping sensitive data out of those labs’ clouds, or you don't expect to run afoul of acceptable usage policies, you'll get much smarter tokens much faster than you will from a local token factory like the RTX Spark, and you can get them on most any reasonably powered traditional PC you have available.

But if you're surfing the AI wave on local hardware for reasons of privacy, offline model availability, freedom to tinker, or just because you think it's cool, the RTX Spark platform blends the memory capacity, compute performance, and software resources you need to get there with the familiarity and broad compatibility of Windows on top. The fact that Nvidia, Microsoft, and their partners have delivered such a capable, compatible, and polished platform even in its first at-bat should put the rest of the PC industry on notice.

Nvidia’s efforts to reshape personal computing will face strong headwinds in the immediate future from the sky-high DRAM and NAND price hikes that the company’s own Vera Rubin rollout is partially responsible for. Perhaps when the dust settles in the next two to three years, and products built around the next-generation RTX Spark Superchips arrive, we can more confidently assess whether the company has accomplished its goal.

For now, the RTX Spark feels like a platform for those who fervently believe that open AI models will continue to get more intelligent within relatively small memory footprints, and that those potential future improvements are worth a hefty chunk of change to get ready for today. Maybe that's a smart bet in a world where compute is an increasingly precious resource. But unless you're a true believer with cash to burn, our first impressions suggest that you should wait until Microsoft and Nvidia fully lay the software foundation needed for this platform to deliver on its promises.