Paperspace

vs

Vultr

An alternative to Vultr for more GPU options and better scalability

Vultr is unique in the context of most other GPU cloud providers in the sense that the only way to get dedicated GPUs is to spin-up a 4x or 8x cluster of NVIDIA A100s as bare metal instances. This may be a feature to some and a bug to others.

By contrast, Paperspace matches the highest offering of Vultr (8x A100 80GB) but also offers more than a dozen different types of GPUs, offers Linux and Windows virtual machines, and offers an entire suite of deep learning software called Gradient to boot.

Wider selection of GPUs

As a GPU provider, Vultr is much like OVH in the sense that only one kind of GPU is offered. This has downsides when it comes to reproducibility and vendor lock-in but the fact that Vultr only offers discrete GPUs as bare metal ameliorates those concerns.

What Paperspace can provide is more options for different use cases. It's not always necessary to run 4x or 8x of the highest end A100 card and Paperspace is great for everything in between while Vultr has one speed.

Up and running faster with starter templates

Paperspace offers a number of pre-tested starter templates in the console to make it easier to get up and running quickly. In particular, the "ML in a Box" template from Paperspace is an Ubuntu instance with all of the most popular machine learning and deep learning libraries and dependencies pre-installed. This saves an enormous amount of time when configuring a server to start running inference. There are also plenty of templates on Paperspace for other usecases such as rendering, streaming, and so on.

Core competences

Vultr is a cloud hosting company that recently started to carry GPUs. Paperspace is a GPU cloud infrastructure company that has been specializing in GPUs in the cloud since 2016. The deep learning users who will be most attracted by Vultr's A100 offerings should note that Paperspace in addition to VMs also makes it easy to spin-up Notebooks, Workflows, and Deployments to make it easier than ever to explore, run, and train models at scale.

Comparing GPUs from Paperspace and

Vultr

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Comparing GPUs from Paperspace and

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Comparing GPUs from Paperspace and

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Comparing GPUs from Paperspace and

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Comparing GPUs from Paperspace and

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Comparing GPUs from Paperspace and

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Comparing GPUs from Paperspace and

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Comparing GPUs from Paperspace and

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Comparing GPUs from Paperspace and

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Comparing features from Paperspace and

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Linux
Windows
SSH
Public IP
Free GPUs
Jupyter
Shared Persistent Storage
10+ GBPS
AMD Support
CPU-only Support

Why is Paperspace a great alternative to Vultr when it comes to GPU machines?

Paperspace has a much wider catalog of GPUs with far more configuration options. While Paperspace can provide the exact machine specifications that Vultr offers, the same is not remotely true in the inverse. Paperspace also has a suite of starter templates specifically developed for deep learning users to make it faster and easier than ever to start running production deep learning applications.

Looking for GPU cloud pricing information?

Check out the Ultimate Guide to GPU Cloud Providers! It's all there!

Did we leave something out?

Or do you have a question about this comparison that isn't answered? Please let us know!

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500K+

Users

100M+

Compute hours

1M+

Jupyter notebooks

Server

Linux SSH

Paperspace Linux machines help you train the most demanding ML models. Scale from a single instance to an entire cluster with a few clicks.

Popular Features

  • Preloaded with ML frameworks
  • Latest NVIDIA GPUs
  • Blazing fast low-cost GPUs
desktop

Windows Desktop

Paperspace Desktops are super fast virtual computers you can use for anything from streaming to rendering to remote work and more.

Popular Features

  • Ultra responsive streaming
  • Shared storage
  • Wide range of CPU & GPU instances

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