ASUS ProArt P14 vs P16: Which Fits Your Workflow?

The ASUS ProArt P14 is the lighter pick; the P16 gives you a larger 4K display, a second SSD slot and a faster card reader. In Canoe’s matched ComfyUI test, the two were nearly tied at 25.5 and 25.4 seconds per image, respectively. ASUS said on October 16 that both models were available for purchase in the United States.

What both models share

Both laptops use NVIDIA RTX Spark N1X, a platform with a CPU and GPU that share system memory. ASUS lists options with an 18-core CPU and 5,120-core GPU, or a 20-core CPU and 6,144-core GPU. Its U.S. product pages advertise up to 128GB of unified memory for each model. That shared capacity can accommodate larger local AI models than a laptop GPU with a smaller pool of dedicated video memory, though capacity alone does not determine speed.

The differences that shape your workflow

ASUS’s U.S. product pages describe a 14-inch 3K screen for the P14 and a 16-inch 4K screen for the P16. ASUS’s Canadian specifications list the P14 at 1.48 kg and the P16 at 1.77 kg, with one M.2 SSD slot in the P14 and two in the P16. Canoe identifies the P14 review unit’s card reader as UHS-II, rated up to 312 MB/s, and the P16’s as SD Express 7.0, rated up to 985 MB/s.

For frequent travel, the P14’s lower listed weight is the clearest practical advantage. The P16’s larger, higher-resolution screen gives more room for a timeline or image canvas, while its second SSD slot and faster reader suit workflows that move footage from SD Express cards or need more internal storage. ASUS’s U.S. pages list SSD capacity up to 1TB for the P14 and 2TB for the P16.

What the AI tests show

Canoe measured 25.5 seconds per image on the P14 and 25.4 seconds on the P16 in one matched ComfyUI test. That result puts the models close in that workload; it does not establish how they compare across other AI tasks.

Canoe also ran Qwen3.8 Flash Next, a 177-billion-parameter model, on its 128GB P16. The model used 67GB and generated 27 to 38 tokens per second in LM Studio after setup. Canoe reports RTX Spark memory bandwidth of 273 GB/s and notes that a laptop RTX 5090 has 24GB of faster video memory. Its practical distinction is capacity versus speed: a conventional laptop GPU may be faster when a workload fits in its 24GB, while the P16 test ran a model that used much more memory.

Local AI setup on Windows on Arm

The P16 result depended on getting the app and GPU runtime configured for Windows on Arm. Canoe reports that LM Studio’s default x64 download crashed when using the GPU under emulation; its ARM64 build initially ran the model on the CPU. The reviewer then selected CUDA 13 (Windows ARM) in LM Studio’s Runtime settings and adjusted GPU memory allocation in the NVIDIA App.

In Canoe’s test, allocating 96GB to the GPU left 32GB for Windows, triggered SSD swapping and reduced generation speed from 38 to 20 tokens per second. That is a result from the reviewer’s setup, not a general performance rule for every allocation. For anyone relying on a particular creative or AI app, Windows on Arm compatibility is part of the laptop decision, alongside screen size and storage.

Canoe’s overnight video playback test used Balanced mode, 50% brightness, Wi-Fi on and muted audio. The reviewer estimated about 12 hours for the P14 and 12.5 hours for the P16 under those conditions. During long AI sessions, Canoe also reported fan speeds above 6,300 RPM.

Which model fits your work?

Choose the P14 if you carry your laptop often and its single SSD slot and 14-inch display fit your work. Choose the P16 if you want a larger 4K workspace, two SSD slots and the faster card reader. Canoe’s ComfyUI result gives no meaningful performance separation between them in that one test; the 128GB P16 local-model run is a specific example of what its shared memory can handle after setup.