[sapiogram]

And that was already impressive. High-end gaming computers with dual-channel DDR5 only reach ~100GB/s of CPU memory bandwidth.

[Aurornis]

High end gaming computers have far more memory bandwidth in the GPU, though. The CPU doesn’t need more memory bandwidth for most non-LLM tasks. Especially as gaming computers commonly use AMD chips with giant cache on the CPU.

The advantage of the unified architecture is that you can use all of the memory on the GPU. The unified memory architecture wins where your dataset exceeds the size of what you can fit in a GPU, but a high end gaming GPU is far faster if the data fits in VRAM.

[NetMageSCW]

The other advantage is you don’t have to transfer assets across slow buses to get it into that high speed VRAM.

[RossBencina]

Right, but high-end gaming GPUs exceed 1000GB/s and that's what you should be comparing to if you're interested in any kind of non-CPU compute (tensor ops, GPU).

[Rohansi]

And you can find high-end (PC) laptops using LPDDR5x running at 8533 MT/s or higher which gives you more bandwidth than DDR5.