
Machine learning benchmark: GPU providers - Radim
https://rare-technologies.com/machine-learning-benchmarks-hardware-providers-gpu-part-2/
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DTE
Really great breakdown! Another important thing to think about when building
out a production or even dev environment are the other associated costs (i.e.
bandwidth, storage, egress, etc). We think that these costs can be pretty
obscene in the public cloud and we try to optimize as much as possible to keep
the total cost down.

Disclosure: I'm one of the founders of Paperspace
([https://www.paperspace.com](https://www.paperspace.com)) and we spend a lot
of time thinking about GPU compute and pricing. Happy to answer any questions
here

~~~
eb0la
There are also other cost to consider:

\- How much time (=cost) do you need to start running (enterprise) \- Time you
need to access the cloud, starting up and closing vms, and getting shared GPU
resources. \- The cost of lock-in. Ecosystems are great; but sometimes you
need the basics working.

Some providers are _much_ better than others at this.

Mandatory Joel on Software mention:
[https://www.joelonsoftware.com/2000/06/03/strategy-letter-
ii...](https://www.joelonsoftware.com/2000/06/03/strategy-letter-iii-let-me-
go-back/)

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bwasti
“IBM Softlayer and LeaderGPU appear expensive, mainly due to under-utilisation
of their multi-GPU instances. The benchmark was carried out using the Keras
framework whose multi-GPU implementation was surprisingly inefficient, at
times performing worse than a single GPU run on the same machine.“ - this is
unacceptable in a benchmark like this. There is an entire software stack that
is influencing performance and much of the hardware is dissimilar.

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zitterbewegung
Another good source of information for GPU deep learning training is a
comparison for graphics cards (This is mainly for researchers though).
[http://timdettmers.com/2017/04/09/which-gpu-for-deep-
learnin...](http://timdettmers.com/2017/04/09/which-gpu-for-deep-learning/)

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lostmsu
No Azure?

