This is why you can’t build your own RTX Spark PC
I’ve been putting the staggeringly expensive $7,000 Asus ProArt P16 laptop through some unusual tests for my AI-focused RTX Spark review, including running a sprawling real-world research workload through OpenAI’s gigantic 120-billion-parameter gpt-oss model. Rather than asking an AI to write a poem about Jensen Huang or make me a picture of a cat on a motorcycle, I wanted to see whether this specialized hardware could handle the sort of stupidly ambitious local AI work I’d normally reserve for cloud models.
It can. More interestingly, my monster desktop can too. Just much, much slower.
For one frozen comparison, both systems chewed through the exact same workload, generating 472,084 tokens along the way. The ProArt P16 RTX Spark finished in 33 minutes and 13 seconds. My desktop needed 52 minutes and 51 seconds.
That’s a 19-minute, 38-second ass-kicking delivered to a fully kitted, fire-breathing high-end desktop packing an RTX 5090.
The reason gets to the heart of what makes these new high-memory AI systems so interesting — and why you can’t simply build your own equivalent with conventional enthusiast PC parts.
128GB isn’t always 128GB
The ProArt P16 RTX Spark packs 128GB of unified LPDDR5X memory, with roughly 96GB available to the GPU during my testing. My desktop pairs its RTX 5090’s screaming-fast 32GB of GDDR7 memory with 96GB of conventional system RAM.
