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Comment by tw04 | Hacker News Reader

You can deploy PCIe with switches over distance and keep the GPUs in an external enclosure:

http://www.dolphinics.com/products/pent-dxseries-dxs410.html

http://www.dell.com/us/business/p/poweredge-c410x/pd

*there are newer/better/faster versions of the above available, just two examples I had handy as my guess of how to make this happen. I'm confident that even if AWS is doing something similar it's being done on customized hardware.

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As a gamer I was initially surprised that works. It makes sense if server side use cased are more batch oriented/compute intensive.

Latency to start and stop "jobs" is critical in gaming as you are trying to hit a 60-144hz job time.

GPUs in the cloud aren't targeted at gamers. They're targeted at people doing things like running render farms and training deep learning models.

What about companies interested in real-time streaming of 3D content? That use case is extremely sensitive to latency, probably more so than normal gaming is.

Fortunately the amount of additional latency introduced is likely to be negligible (another comment cites PCI-E switches incurring <= 1µs).

The added latency of a PCIe switch and some cables is 1 us or less; it should be negligible for almost any workload.

The problem is that transferring data to/from GPUs is already problematic performance wise. I guess it depends on your application whether the data transfer rate hit is acceptable or not.

Looks like the use case that they have in mind is focused on CAD / CAM. Don't see much mention about deep learning and I would guess that isn't being targeted at all.

I'm in the process of moving our local graphics workstations to GRID-powered workstations on AWS, with Raspberry Pi 3 "heads" and 1080p 60fps streams.

The goal is to cut our capital costs, allow access to GPU-based graphics apps "anywhere", and host all of our virtual film production assets on AWS, where they are accessible for rendering, simulation, etc.

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