Asus ProArt P14 (2026, RTX Spark) Review

Design and Keyboard

• Equally fetching in white and silver
• Excellent port selection
• Comfortable keyboard and huge trackpad

The new ProArt, in both 16-inch and this 14-inch flavour, is a devilishly handsome laptop. I’m a particular fan of this smaller form factor, especially as it packs in the same grunt as its bigger brother into an even more svelte shell. Plus, the white and silver colorway my sample comes in gives it the look of a classic MacBook.

It tips the scales at 1.48kg, making it a halfway house between superlight ultrabooks such as the Zenbook A14 (2026) and beefier 14-inch gaming laptops with discrete GPUs inside. For the grunt on offer, it makes for a thin and reasonably light option.

This ProArt P14 is also just 13.9mm thin, making it one of the thinnest options in Asus’ creative-oriented range. More impressively, though, it maintains parity with the bigger P16 in terms of the ports on offer –  on the left, that means a pair of 40Gbps USB4-capable Type-C ports, plus a full-size HDMI and a 3.5mm headphone jack, while the right side has a full-size SD card reader, a further USB4 Type-C port and a USB-A port.

Open up the lid, and you’ll find a capable small form factor keyboard, with deeper travel than you’ll find on a competing MacBook Pro. It’s a comfortable and smooth keyboard to type on, with some pleasant tactility that makes work easy, plus it benefits from a bright white backlight for after-dark use.

The trackpad also retains its lovely qualities, being typically huge and providing responsive and accurate tracking. It also supports the modern gesture controls for Windows and has pleasant haptics, although if you were a fan of the DialPad jogwheel Asus kitted out to previous ProArt laptops, then you’re out of luck, as it isn’t here.

Display and Sound

• Gorgeous tandem OLED screen
• Detailed and vibrant output
• Lovely audio for a laptop

Asus is offering parity across the screen sizes for this new ProArt with a similarly outstanding screen to the one you’ll find on the P14’s bigger brother. This means we’ve got a 14-inch 3K, or 2880×1880, resolution Lumina Pro OLED screen, complete with a 120Hz refresh rate for smoother on-screen action to aid zippiness for productivity and gaming tasks.

As with other ProArt models, this new option retains a tandem OLED screen that stacks layers of OLED panels atop one another to yield higher brightness and potential efficiency figures. We’ve seen them fitted to everything from TVs to the latest iPad Pro models in recent years, as well as laptops and more monitors in recent times.

We’ve got deep blacks and immense contrast, with my colorimeter measuring 0.01 and 45570:1, respectively, while the 6800K white point is round and about where it should be.

My colorimeter also measured peak SDR brightness of 624.3 nits, meaning displayed images have some real punch and is, intriguingly, a fair whack higher than the 16-inch model. HDR can push this as high as 1600 nits in supported content with this tandem OLED variant.

The colour accuracy is a highlight too, as you’d perhaps expect with an OLED panel. It hasn’t actually improved at all from the older panel, though, not that it needed to. We’ve got perfect 100% coverage of both the sRGB and DCI-P3 gamuts, as well as 94% Adobe RGB coverage. With it, it proves this laptop’s impeccable credentials for work with both colour-sensitive and productivity workloads.

Its speakers also remain some of the best in the business for a set of laptop units, with excellent depth, body and clarity. They rival those on my 16-inch M1 Pro MacBook Pro, which is an achievement in itself, considering how meagre some laptop speakers can be.

Performance

• Beefy 20-core CPU
• Standout integrated GPU results
• Capable local AI performance

As covered in the ProArt P16 review, I’ve taken the opportunity to shake up the benchmark suite given the watershed moment RTX Spark feels like it could be for Windows laptops.

For reference, the same tests as before are present for legacy comparisons. However, this has now been supplemented with some UL Procyon tests and a new ML Perf AI benchmark on the synthetic side. Likewise, this also includes some additional 3DMark gaming tests that are a little more modern than good old Time Spy.

It’s now time to talk about the meat on the bone – what RTX Spark actually is. Nvidia describes RTX Spark as a “superchip”, and the N1X looks to be one of the beefiest SoCs you’ll find in a laptop, pairing a 20-core Grace CPU that was co-developed with MediaTek (split between 10 Cortex X-925 P and 10 Cortex-A725 E cores) with an RTX Blackwell integrated GPU, complete with 6144 CUDA cores for raw grunt in equivalent numbers to an RTX 5070. That’s all running with a scalable TDP of between 45W and 80W, depending on the device, which is mighty impressive on the face of it.

This is paired with up to 128GB of unified memory (although the ProArt P14 sample I have has 32GB inside) that can run at speeds upwards of 9400MT/s, alongside having up to 300GB/s of memory bandwidth through a 256-bit bus. 

Configurations of RTX Spark with 24GB and 32GB are available, too, with an 18-core CPU and a slightly cut-down GPU with 5120 CUDA cores. For the full variant, it starts at 24GB and rises up to 128GB, with prices scaling accordingly.

The benefit of such high quantities of unified memory isn’t just a surefire allocation of VRAM for the integrated graphics, but is more to do with arguably the entire point of why these laptops exist – local AI. We’re getting to a point where local AI models are beginning to catch up to cloud-based models in overall intelligence, and it’s starting to be the case that you can have a potent AI model running locally on your laptop, without data needing to be sent to the cloud.

As a very basic example, the Claude Opus 4.7 Max model that saw Anthropic introduce CoWork to the AI’s list of abilities launched in April, and has an AA intelligence rating of 41. The Qwen 3.8 Flash Next model that released in August can run locally and has an AA intelligence rating of 40. It proves local AI is catching up to data centres purely in intelligence alone.

The other big argument here is cost. An agent that runs on-device can run unmetered, without you having to necessarily think about cost. Nvidia’s recent briefing discussed Claude Sonnet 5.5 Medium – to run 61 million output tokens a month would cost $1200 in API fees, while using the Qwen 3.8 Flash Next model on an RTX Spark laptop with the same number of tokens will cost you $20 in electricity. If you’re an intensive user of agents, the argument here is that the device can eventually pay for itself.

The interesting thing with this ProArt P14 sample is that it only comes with 32GB of RAM, making it one of the theoretically more wallet-friendly variants available, so it’s intriguing to see how that plays out for both traditional benchmarks and for AI workloads, too.

To delve into the actual benchmarks, To run through the actual benchmark numbers, Geekbench 6 gives scores of 3,003 single-core and 19,171 multi-core. That matches the Core Ultra X9 388H in the Zenbook Duo (2026) in single-core (3,003) and edges ahead in multi-core (17,285), and it’s comfortably clear of the Ryzen AI Max+ 395 in the Asus ProArt PX13 GoPro Edition (2,808 and 13,271). Only the Snapdragon X2 Elite Extreme in the Asus Zenbook A16 (2026) is faster, at 3,711 and 21,940.

Cinebench R23 is built for x86, so the RTX Spark’s emulated result undersells it, particularly in single-core, where its 1,306 trails the X2 Elite Extreme (1,579) and native x86 chips such as the Panther Lake X9 388H (2,165). Multi-core tells a better story. Its 14,650 just edges the Zenbook A16 (14,569) and sits well ahead of the ProArt PX13 (12,785), though the 388H (16,417) is a fair way in front.

Cinebench 2026 provides a different frame of reference that puts the RTX Spark’s single-core performance as quite strong, with the ProArt P14 scoring 532. Using the benchmark’s own built-in comparisons, this puts it roughly between M3 and M4 in MacBook terms, and similar to Intel’s top-end Panther Lake chips. Multi-threaded performance, as you’d expect from an SoC with 20 cores, is strong too, at 5,994, sitting between the same Apple M-series silicon and in line with top-tier Panther Lake. For MediaTek cores that are a couple of years old in raw terms, that’s a strong showing.

The GPU side is impressive as well. The customary 3DMark Time Spy test returned 10,131, more than double the Adreno graphics in the Zenbook A16 (4,317) and well clear of the potent B390 in Intel’s top Panther Lake chips (7,189). It’s also ahead of Strix Halo as tested in the Asus ProArt PX13 GoPro Edition (9,569), and not far off some discrete RTX 5070-powered laptops I’ve used, such as the Medion Erazer Deputy 15 P1. Elsewhere, it managed 2,565 in Steel Nomad, 19,281 in Fire Strike and 2,569 in Speed Way. In Procyon’s AI Computer Vision test, it scored 3,313.

Storage is rapid, too, with the SSD hitting sequential reads of 7241.66 MB/s and writes of 5847.18MB/s, while the 1TB capacity is reasonably strong. 32GB of unified memory feels a little poor at this price, though.

On the point of gaming, the P14 is a little constrained next to the original ProArt P16 I tested, which came with a discrete RTX 5070 laptop GPU. At 1080p, it recorded 61.53fps in Cyberpunk 2077 and 55fps in Returnal.

Bumping up the resolution to QHD, the results are still playable, with a 40.83fps average in Cyberpunk and 41fps in Returnal. That said, ray-traced Cyberpunk at its Ultra preset is a step too far, with 27.05fps at 1080p and just 13.82fps at the panel’s native resolution.

RTX Spark can take full advantage of Nvidia’s RTX stack, including the clever second-gen DLSS Transformer model, a marked improvement over the original. It lifts rasterised performance to 66.87fps at 1080p and 43.16fps at native resolution. Turn on ray tracing alongside it and you get 37.05fps and 24.73fps, which is far more usable than without. There is also support for DLSS 5, with a new update unlocking a model that’s two times faster in supported titles, such as NBA 2K27.

You also get access to multi-frame generation in supported titles, which adds up to three ‘fake frames’ for every traditionally generated one for a perceptibly smoother experience. New updates to DLSS have added a further 6x multiplier, and a ‘Dynamic’ one that automatically scales the output FPS based on a target you set.

The addition of these frames relies on a high enough base frame rate to stop the displayed image feeling choppy or laggy. With the maximum 4x multiplier applied, the ProArt P14 managed 186.08fps at 1080p in Cyberpunk 2077 and 104.10fps at native resolution. With ray tracing applied, these were 117.58fps and 74.22 fps, respectively.

As mentioned above, AI is a big part of the draw of RTX Spark. The P14 scored 3313 in UL Procyon’s AI Computer Vision benchmark 2.0, for reference. ML Perf is more revealing, though. It measures tokens per second (TPS), which is how quickly a model writes, and time to first token (TTFT), which is how long you wait for it to start. Run on the GPU with llama.cpp and Microsoft’s Windows ML, Llama 3.1 8B averaged 52.0 TPS and 43.2 TPS, respectively, so llama.cpp has a clear edge here. The smaller Phi 4 Mini is quicker, hitting 83.0 TPS on llama.cpp against 76.3 on Windows ML, and llama.cpp gets the first token out sooner in both cases. The heavier Phi 4 Reasoning 14B still manages 30.0 TPS on llama.cpp and 24.7 on Windows ML, which is faster than most people read.

The task matters more than the runtime. Short jobs like content generation feel almost instant, with the first token arriving in roughly a tenth of a second or less on the 8B-class models. Summarising a longer piece is the toughest test. Llama takes about 1.8 seconds to start on Windows ML, and speeds dip to 40.0 TPS.

In the SWE Agent test, an agentic workload, Llama 3.1 8B averaged 50.4 seconds on llama.cpp, while Qwen 3 8B took 52.3 seconds. The P14 looks quite comfortable dealing with those eight-billion parameter models.

Software

• Wholesale changes to Windows to accommodate local AI
• Lower unified memory quantity limits model selection
• Some Asus-specific software present

The ProArt P14 comes running Windows 11, although Microsoft has made numerous changes to the Windows experience to better suit RTX Spark, all of its AI horsepower, and the rise of agentic AI.

Microsoft is building the next generation of Windows around trust as much as intelligence. As agents move the OS away from being an app-based frontend, it is changing how Windows handles containment, identity and manageability for agents. Its Microsoft Execution Containers (MXC) give each agent its own lane, with OS-enforced limits on what it can access. Each agent also runs with its own credentials, though the user stays in charge as admin. Anthropic, OpenAI and OpenClaw are among those building on MXC, giving it some hefty backing alongside Nvidia’s own push for local AI.

The other half of the plan is “hybrid intelligence”, where AI runs locally on-device and only reaches into the cloud when you need it to. Windows ML provides the runtime across CPU, GPU and NPU, while HydraFusion routes tasks between local and cloud models to get more from your token budget. Microsoft has mentioned a 284-billion-parameter DeepSeek V4 Flash frontier-class model that can be squeezed into 60GB of memory, and Nvidia’s own Nemotron 3.5 Bolt E4B offers a 74-billion-parameter model in just 24GB of memory thanks to 2-bit quantisation and having only nine billion parameters running at any one time. It’s pitched as a natural fit for RTX Spark and for more general-use tasks. In Copilot, a new toggle gives the assistant local file access and permission-gated actions, while Microsoft has also updated the taskbar search for contextual quick actions, such as arranging windows, enabling dark mode or do-not-disturb.

Microsoft is adding an onboarding system to Windows to make running local models something anyone can do, which is accessed through traditional Windows setup with an online account.

My own process was a little fiddlier, as I’d deliberately set the laptop up with an offline account. I downloaded the Hermes agent, then picked a model that could live in the system RAM. I thought that Qwen 3.8 27B would be happy with 32GB of system RAM, although Hermes stated the 19 or so GB model was too large by default, even though I had dialled the iGPU’s allocation back to 10GB, rather than its out-of-the-box 12GB.

What this led me to do was use a quantised version of the same model – to be specific, it was Qwen 3.8-27B IQ3_S. This essentially means it’s a heavily compressed version of Qwen’s 27B model to get it running, which trades some answer quality for fitting in memory at all. Nonetheless, I picked this model because it handles images as well as text, and is aimed at agentic work, which is exactly where this hardware is being pitched.

To try out an agentic workload, I set up Himalaya to give Hermes live access to my Gmail, restricted to read-only through one of Gmail’s app passwords. I then asked it to sort through the pitches and requests in my inbox, highlight the interesting ones, and give higher priority to products within my remit with a reasonable deadline. For each, it had to assign a score and record it to a local file on the laptop.

Getting there wasn’t entirely smooth. Hermes initially wouldn’t install and run the quantised version of Qwen 3.8 27B, as it couldn’t find it, and I had to do some immense troubleshooting and redownloading for it to properly install. 

Once running, the task took around an hour, but the result was a document in priority order, based on the samples it identified and when they were embargoed until. It also notified me of the most important ones and recommended next steps. As always, the quality of the output depends on the quality of the input, and the length of time was likely down to my initial prompt being so detailed. During this time, the fan noise did get quite loud while the P14 worked away, but it acted as an intriguing demonstration of using local AI like an office junior.

Beyond the agentic AI elements, the ProArt P14 features a couple of Asus-specific apps pre-installed. These include GlideX, which is where you can manage tasks such as casting or mirroring the screen to other devices wirelessly, or transfer files across the same network. You can also enable remote access to a mobile device, too.

The StoryCube app is designed as another means of organising photos and videos, using AI to recognise faces and file your photos for you, which is handy. MuseTree comes pre-installed, which is an AI image generator.

There is also enough AI horsepower inside to mark this laptop as a Copilot+ PC, providing access to Microsoft’s AI functionality for generative powers and filters in the Photos and Paint app, as well as the clever Windows Studio webcam effects for background blurring, auto framing and maintaining eye contact.

Battery Life

• Lasted for 13 hours 43 minutes in the battery test
• Capable of lasting for one to two working days

The ProArt P14 bundles in a 90Whr cell, which is one of the heftier batteries you’ll find on an ultrabook today. It’s larger than the cell fitted to some older (and bigger) ProArts, and combined with the efficiency of RTX Spark, I had very high hopes for this laptop’s battery life.

The PCMark 10 battery life test wouldn’t play ball with this laptop, weirdly, so a 1080p video loop test at the requisite 150 nits of brightness had to suffice. With this in mind, the ProArt P14 lasted for 13 hours and 43 minutes before conking out. This easily meets our longstanding 10-hour target and means you’ll nearly get two working days out of this laptop.

It’s also three hours or so longer than the RTX 5070-powered ProArt, beats the MacBook Pro M4 by nearly two hours, and wins out against the Strix Halo-powered ProArt PX13 GoPro Edition by a similar amount. Although it’s behind other ultrabooks we’ve tested – for reference, the Zenbook A16 (2026) lasted for 19 hours or so with its Snapdragon X2 Elite Extreme inside.

Asus has also bundled this laptop with a beefy 140W charger that, unsurprisingly, puts power back into the ProArt P14 rather briskly. It took just 27 minutes to get it back to 50 percent, while a full charge took 90 minutes.

Should you buy it?