[icelancer]

vast you have to choose specific machines. gpudeploy routes to whatever resources are available.

vast has a lot of bad machines with terrible PCIe lanes and architecture you have to learn the hard way. Someone on HN wrote a script to run a test docker image on every machine and auto-tagged the machines' quality using their API, which is what I'd do if I was going to use vast seriously for compute.

[lelanthran]

>> Im also interested in what the differing factor is.

> vast has a lot of bad machines with terrible PCIe lanes and architecture you have to learn the hard way.

Wouldn't gpudeploy have exactly the same problem? How is it mitigated with gpudeploy?

[everforward]

I think it's more of a business strategy issue than a technical one.

I suspect it would be trivial for Vast or GPUDeploy to spin up a benchmarking job before allowing sales on that machine. I'm not an expert on PCIe lanes, but I would think the performance issues would be visible via bandwidth or latency on the lanes.

It kind of makes sense to me, though. If I were looking for absolute reliability and was willing to pay for it, I'd just go to one of the many GPU cloud vendors. Likewise, I suspect anyone willing to really work on getting good performance would rather be a real provider or sub-provider than being part of this nebulous C2C GPU cloud.

[polygot]

Do you have a link to that thread/Docker image? I would be very interested using it

[icelancer]

I don't, sorry. I would love to use it as well, I should've bookmarked it! Also, I'm not sure the person opensourced it.