
Nvidia Tesla V100 on Google Cloud Platform - deesix
https://cloudplatform.googleblog.com/2018/04/Expanding-our-GPU-portfolio-with-NVIDIA-Tesla-V100.html
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singhrac
I'm sure the extra customizability is great and all, but isn't this not price
competitive with Amazon's P3 instances? For spot pricing at least, they're at
$1.2/hr [1], whereas here it's $1.34/hr [2]. It's not a big difference, but is
there a benefit Google provides that I'm missing?

edit: I see now - the big difference is that Google is offering P100s (and
pretty cheaply), which are not available on AWS.

[1]:
[https://aws.amazon.com/ec2/spot/pricing/](https://aws.amazon.com/ec2/spot/pricing/)

[2]:
[https://cloud.google.com/products/calculator/#id=56e6b672-18...](https://cloud.google.com/products/calculator/#id=56e6b672-1871-4c11-bf40-6f1e2994a9c3)

~~~
boulos
Disclaimer: I work on Google Cloud (and launched preemptible VMs as well as
contributed to our GPU efforts).

That's for 1xV100.

The interesting action is in multiple GPUs at once, for say deep learning. The
spot market is fairly non-linear in pricing, so the 8xV100 is more than 8x as
much, while it's strictly linear for Preemptible.

Similarly, you have to attach a lot of cores (Broadwell only) and RAM to the
p3s, which is often unnecessary for GPU applications (you often want 1-2x the
total GPU memory depending on your application, but rarely do you need 64
vcpus to go with them).

Additionally, if for some reason you want super fast local SSD to go with your
GPUs (say a GPU accelerated database), you can do so on GCE.

tl;dr: linear, flexible pricing that usually wins (plus Skylake, local SSD and
more).

~~~
singhrac
Those are all good points - I was focusing on my use case (i.e. small deep
learning jobs), but definitely locking into pre-built configurations can be
wasteful. I'll likely use GC anyway, but appreciate the extra reasons given.

~~~
boulos
Either way, I'd encourage trying out multi-GPU training. The latest NVLINK is
pretty impressive, and reducing your iteration time for free (because linear
pricing) is pure goodness.

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ntenenz
Unfortunately, these are of the older gen w/16GB of RAM rather than 32GB which
is now available. [https://www.nvidia.com/en-us/data-
center/tesla-v100/](https://www.nvidia.com/en-us/data-center/tesla-v100/)

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minimaxir
The real plus of V100/Volta is better performance half-precision/FP16 training
of deep learning models, which surprisingly isn't mentioned in this post.
(more info on NVIDIA's site: [https://devblogs.nvidia.com/inside-
volta/](https://devblogs.nvidia.com/inside-volta/))

~~~
jlebar
_receiving up to 1 petaflop of mixed precision hardware acceleration
performance_

It's there, it's just slathered in marketing-speak. :)

~~~
minimaxir
Fair. :P

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reilly3000
This is interesting as it seems to compete with their Cloud TPU offering. If
benchmarks hold up against this as they looked here:
[https://www.forbes.com/sites/moorinsights/2018/02/13/google-...](https://www.forbes.com/sites/moorinsights/2018/02/13/google-
announces-expensive-cloud-tpu-availability/#75ed5416359f)

... then V100 would win every time. It offers:

    
    
      - Proven/Familiar platform in CUDA.   
      - No specific cloud provider lockin
      - Similar/Better $/performance on FP16 while maintaining FP24/32 as an option.
    

What am I missing?

~~~
dgacmu
The more recent RiseML benchmkark: ([https://blog.riseml.com/comparing-google-
tpuv2-against-nvidi...](https://blog.riseml.com/comparing-google-
tpuv2-against-nvidia-v100-on-resnet-50-c2bbb6a51e5e)

And the HN discussion of that benchmark, for context:
[https://news.ycombinator.com/item?id=16931394](https://news.ycombinator.com/item?id=16931394)

