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It's possible that "many" firms do this, but given AWS' growth numbers that would imply they don't really spend meaningful amounts to begin with. Counterpoint:

by lvh 6y ago

It's possible that "many" firms do this, but given AWS' growth numbers that would imply they don't really spend meaningful amounts to begin with. Counterpoint: I've seen a great many false economies with people trying to go on-prem and do alternative hosting because they don't think the AWS premium for e.g. GPU instances is worth it. I don't think that has generally worked out well.

KingOfCoders 6y ago
"It's possible that "many" firms do this, but given AWS' growth numbers that would imply they don't really spend meaningful amounts to begin with." I could spend a million EUR a year on AWS without the need for most of the services of AWS? But it YMMV and 1M EUR/y is not meaningful, perhaps we differ there.
qaq 6y ago
There is no good data to prove either statement. You could def prove that for some large scale onprem for a given type of problem is cheeper than the cloud.
freeone3000 6y ago
The AWS premium for GPU instances is absolutely not worth it. You don't hear about people running local GPU compute clusters because it's not newsworthy -- it's obvious. Put a few workstations behind a switch, fire up torch.distributed, you're done. And after two months, you've beaten the AWS spend for the same amount of GPU compute, even if only 50% of the time is spent training. Timesharing is done with shell accounts and asking nicely. You do not need the huge technical complexity of the cloud: it gets in the way, as well as costing more!
- srtjstjsj 6y ago
  What if you want 10x the GPU for one month to build a model?
  - freeone3000 6y ago
    That's the only scenario I can think where it comes out clearly in favor of AWS - you've tested your model in the small on Colab, you're confident you'll need only a few training runs, you can schedule them in us-east, and you can inference on CPU, and you won't need to rebuild for another eight months (when purchased cards become outdated). It's not an impossible scenario... But imagine the sort of company that trains their own model instead of using a huggingface refinement or an off-the-shelf redistillation. (These can be done reasonably on an average gaming PC, no need for a cluster.) Such a company has expensive human resources. They bothered to get a data scientist and at least a research engineer, if not a full researcher. Were they hired on six-month contract as well? This is a huge expense, so it must be an important differentiator to have built a custom model -- and it's a one-and-done? I don't see it. I think it's going to be an ongoing project, or it shouldn't have been approved in the first place.

by lvh 6y ago

KingOfCoders 6y ago

"It's possible that "many" firms do this, but given AWS' growth numbers that would imply they don't really spend meaningful amounts to begin with." I could spend a million EUR a year on AWS without the need for most of the services of AWS? But it YMMV and 1M EUR/y is not meaningful, perhaps we differ there.

qaq 6y ago

There is no good data to prove either statement. You could def prove that for some large scale onprem for a given type of problem is cheeper than the cloud.

freeone3000 6y ago

The AWS premium for GPU instances is absolutely not worth it. You don't hear about people running local GPU compute clusters because it's not newsworthy -- it's obvious. Put a few workstations behind a switch, fire up torch.distributed, you're done. And after two months, you've beaten the AWS spend for the same amount of GPU compute, even if only 50% of the time is spent training. Timesharing is done with shell accounts and asking nicely. You do not need the huge technical complexity of the cloud: it gets in the way, as well as costing more!

srtjstjsj 6y ago
What if you want 10x the GPU for one month to build a model?
- freeone3000 6y ago
  That's the only scenario I can think where it comes out clearly in favor of AWS - you've tested your model in the small on Colab, you're confident you'll need only a few training runs, you can schedule them in us-east, and you can inference on CPU, and you won't need to rebuild for another eight months (when purchased cards become outdated). It's not an impossible scenario... But imagine the sort of company that trains their own model instead of using a huggingface refinement or an off-the-shelf redistillation. (These can be done reasonably on an average gaming PC, no need for a cluster.) Such a company has expensive human resources. They bothered to get a data scientist and at least a research engineer, if not a full researcher. Were they hired on six-month contract as well? This is a huge expense, so it must be an important differentiator to have built a custom model -- and it's a one-and-done? I don't see it. I think it's going to be an ongoing project, or it shouldn't have been approved in the first place.