My guess is that just the raw data size, combined with the physical limitations of your RU, makes it hard for the GPU to be fully utilized. Instead you will always be stuck on CPU (decompressing/interpreting/uploading parquet) or bandwidth (transfer from s3) being the bottleneck.

Seems that they are targeting a low-to-no overhead path from s3 bucket to GPU, by targeting: same compression/faster random access, streamed encoding from S3 while in flight, zero copy to GPU.

Not 100% clear on the details, but I doubt that they can actually saturate the cpu/gpu bus, but rather just saturate the GPU utilization, which is itself dependent on multiple possible bottlenecks but generally not on bus bandwidth.

That's not criticism: it literally means you can't do better unless you improve the GPU utilization of your AI model.