Tired of struggling to get an AWS EC2 gpu instance to work when you just need a GPU machine? If you're looking to run fast GPUs in the cloud with minimal overhead, Paperspace is the EC2 alternative you've been looking for.
With Paperspace, you can run a wide selection of GPU machines (even wider than AWS offers) on Linux or Windows. Paperspace offers persistent storage, fast network speeds, public IPs, and much more.
Paperspace is also far easier to configure and get started than AWS EC2 -- with friendly billing, invoicing, and customer support.
Paperspace offers more varieties of GPUs than AWS EC2. In particular, Paperspace carries a number of popular mid-range GPUs like the RTX A4000, RTX A5000, and RTX A6000, which provide excellent cost/performance value due to the large amount of GPU memory. Paperspace offers more types of GPUs and more configuration at the GPU level.
AWS EC2 makes it difficult to manage billing, approvals, and collaborators. That's because AWS (and specifically the IAM identity access layer) is geared toward enterprise users. Paperspace is focused on developers and teams -- so there's a simple interface for managing your team, viewing invoices, and spinning up and down resources.
With AWS EC2, the high-end instances like the A100 80 GB are only available as 8-way clusters. Paperspace matches the high-end offerings of AWS but critically offers more flexibility than AWS. In the case of the A100 80 GB, Paperspace offers 1x, 2x, 4x, and 8x configurations. This makes it far easier to get up and running on a high-end machine on Paperspace.
Many users report that it's far easier to get up and running with GPUs in the cloud on Paperspace than it is on AWS EC2. The Paperspace GPU cloud is a developer and team-focused product designed with the user in mind, while AWS EC2 is designed for enterprise-level deployments.
Some AWS EC2 GPU users report that certain GPUs are seldom available as advertised. Users also report that the approval process for GPU instances on AWS can be frustrating. On Paperspace, you can see what GPUs are available in real-time in the console. And the approval process is straightforward and speedy.
Finally, many users report that AWS has virtually nonexistent customer support. With Paperspace, there are support engineers standing by day and night to solve your issues expediently.
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!"
