5 ms·

It is still vastly cheaper to buy your own GPUs and ML accelerators than to rent from cloud providers for any reasonable level of utilization (i.e. not a hobby)

It is still vastly cheaper to buy your own GPUs and ML accelerators than to rent from cloud providers for any reasonable level of utilization (i.e. not a hobby).

BeeOnRope 5y ago
My point was that there are accelerators in the cloud which are not available to purchase by the general public and for a time the same applied also to GPUs in a practical sense (i.e., GPUs were being sold in principle but were out of stock for months on end). Depending on your individual time value of money, it can make sense to rent even a high rate so you can access this hardware now.
ip26 5y ago
I'm excited on behalf of the scientists I know for cloud HPC. I see a lot of "develop program locally on tiny subset of data" followed by "how the hell am I ever going to run the full model for the final run on all the data!?" A couple hours machine time leased on a behemoth is really the ticket, and not really that expensive.
- zozbot234 5y ago
  The cost of data egress from the cloud really limits its potential for most HPC applications, though. The "free" or reasonable-cost tiers are okay for small-scale toy use, but not much more than that.
  - AitchEmArsey 5y ago
    For academia, all 3 major cloud providers have a data egress waiver - meaning that as long as data egress is less than 15% of the total bill then it is free. Combine this with the fact that S3/Glacier make much more sense than local HDDs for long-term archiving and egress turns out not to be a significant factor in my experience.
xhkkffbf 5y ago
There's some truth to this, but for many sporadic jobs the cloud can be quite useful. My favorite applications are the streaming video services that have most of their traffic on Friday and Saturday night. You're right that the prices are too high for long, constant workloads, but not about this only being hobbies.
majormajor 5y ago
A lot of legitimate uses out there for occasional, important-when-it's-needed, on-demand compute that works out to very low utilization overall.

It is still vastly cheaper to buy your own GPUs and ML accelerators than to rent from cloud providers for any reasonable level of utilization (i.e. not a hobby).

BeeOnRope 5y ago

My point was that there are accelerators in the cloud which are not available to purchase by the general public and for a time the same applied also to GPUs in a practical sense (i.e., GPUs were being sold in principle but were out of stock for months on end). Depending on your individual time value of money, it can make sense to rent even a high rate so you can access this hardware now.

ip26 5y ago

I'm excited on behalf of the scientists I know for cloud HPC. I see a lot of "develop program locally on tiny subset of data" followed by "how the hell am I ever going to run the full model for the final run on all the data!?" A couple hours machine time leased on a behemoth is really the ticket, and not really that expensive.

zozbot234 5y ago
The cost of data egress from the cloud really limits its potential for most HPC applications, though. The "free" or reasonable-cost tiers are okay for small-scale toy use, but not much more than that.
- AitchEmArsey 5y ago
  For academia, all 3 major cloud providers have a data egress waiver - meaning that as long as data egress is less than 15% of the total bill then it is free. Combine this with the fact that S3/Glacier make much more sense than local HDDs for long-term archiving and egress turns out not to be a significant factor in my experience.

xhkkffbf 5y ago

There's some truth to this, but for many sporadic jobs the cloud can be quite useful. My favorite applications are the streaming video services that have most of their traffic on Friday and Saturday night. You're right that the prices are too high for long, constant workloads, but not about this only being hobbies.

majormajor 5y ago

A lot of legitimate uses out there for occasional, important-when-it's-needed, on-demand compute that works out to very low utilization overall.