Asus ProArt P16 review: This 128GB RTX Spark laptop beat my RTX 5090

The ProArt finished almost 20 minutes earlier, making it roughly 1.6 times faster at completing this particular monster workload. The 5090 run generated somewhat more completion tokens, 92,270 versus 79,903, so don’t read this as a laboratory-perfect tokens-per-second comparison, and definitely don’t read it as “RTX Spark is faster than the RTX 5090 at AI.” It isn’t. Give the 5090 a workload that fits comfortably inside its 32GB of VRAM and its vastly greater horsepower can run wild.

But gpt-oss-120b doesn’t fit comfortably inside 32GB. My desktop can lean on another 96GB of conventional system RAM, but the model is working across separate pools of memory. RTX Spark’s Grace CPU and Blackwell GPU share one gigantic pool, and I’d configured the ProArt to make roughly 96GB available to the GPU, though my test crossed over into system memory there, too. For this oversized workload, that architectural difference mattered enough for the much slower laptop to finish first. By a lot.

The ProArt paid for it in battery life. I started the run at 100 percent; 33 minutes later it sat at 54 percent. But holy shit, that’s interesting. Nvidia stuck 128GB of unified memory in this thing so it could tackle enormous AI models, and I’d found a real workload that genuinely put it to use. This 3.9-pound laptop finished the job almost 20 minutes faster than the monstrous RTX 5090 desktop sitting beside it.

The $7,000 question

Of course, local AI isn’t free. This laptop costs 7,000 freaking dollars before we even start talking about electricity, depreciation, or the frankly stupid number of hours I’ve spent screwing around with models and prompts. But the scale of my workload had gotten pretty stupid, too.