Although Europe's biggest hosting provider, OVH offers only the NVIDIA V100 GPU in 16 GB and 32 GB configurations. While this GPU has long been seen as one of the premier accelerators for deep learning, it has been outstripped by the NVIDIA A100 (available in 40 GB and 80 GB configurations) as well as a number of other Ampere and higher GPU cards with better price-to-performance ratios.
The net result is that while OVH is a trusted cloud provider the same cannot be said about OVH as a GPU cloud provider.
Where else would we start? While OVH features the NVIDIA V100 GPU, Paperspace offers more than a dozen different NVIDIA GPUs with various configuration options. Using a GPU cloud provider with at least several options for GPUs is important for reproducibility and to avoid vendor lock-in long term. Paperspace is up to the task.
The max GPU machine you can get with OVH is a 4x V100 32 GB. This is no slouch of a cluster, but Paperspace offers tremendously better performance options all the way up to 8x A100 80 GB! If high-end performance matters, Paperspace is a great option here because of all the newer and more powerful Ampere series (A4000, A5000, A6000, A100, etc) GPU machines.
Paperspace offers more than VMs with high-performance GPU compute -- Paperspace also offers Notebooks, Workflows, and Deployments for deep learning users via Gradient. If there's a time you need to spin-up a data science notebook or deploy a model to an endpoint quickly and easily then Paperspace can help.
OVH is a good bet for GPUs if your requirements are to run batch jobs on NVIDIA V100s e.g. for processing, rendering, inference, etc. Because of the immense popularity of the V100 GPU, OVH is also able to limit your downside risk slightly in using a GPU cloud provider that only offers a single GPU.
That said, Paperspace provides the same level of scalability and batch processing power -- but with far more options when it comes to GPUs, including newer, more powerful, and more performant GPUs.
Check out the Ultimate Guide to GPU Cloud Providers! It's all there!
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"For ML applications, I’ve found @HelloPaperspace to have the best UI / UX by far"
"I'm very impressed with @HelloPaperspace GPU cloud and an ability to create templates. One API call and 5 minutes later I'm training physics-informed neural networks through http://SIML.ai's environment (@nvidia Modulus + JupyterLab + VS Code + Tensorboard + netdata)."
"Have been using @HelloPaperspace Gradient Notebooks and it has been an amazing experience so far. ... A true local-like development environment feel 😄"
"I just checked out @HelloPaperspace and wow its soooo beautiful"
"I came across a very exciting feature on Paperspace: they mounted additional storage to every machine for free. That storage has public machine learning datasets. OMG, this is so cool. Great job @HelloPaperspace!!! 👏"
"Trying out @HelloPaperspace after all the problems with colab so far the transparency about what you're getting for your money (and what instances are available) is nice. But all the system information graphs are my favorite."
"Just tried Gradient from @HelloPaperspace. Man that thing is super easy to use. #MachineLearning #CloudComputing"
"First time using @HelloPaperspace. Great way to spend more time learning and practicing ML rather than debugging / setting up a Cloud instance."
"We're testing deployment to @HelloPaperspace GPU cloud. So far it works great! Next week we'll add possibility to launch http://SIML.ai instance on it through Model Engineer - one click and you'll be up-and-running!"
