Jarvis Labs is a startup that provides GPU compute to data scientists and students, particularly in India. With a solid selection of GPUs that represent good value for the performance, Jarvis Labs is off to a good start helping users take advantage of GPU compute with minimal headache.
While Paperspace and Jarvis share the goal of helping users get up and running with cloud GPUs quickly, Paperspace offers a large number of options, features, and configurations that Jarvis does not offer.
Jarvis asks users to fill and recharge a wallet with a set amount of funds to power GPU machines. With Paperspace you can set maximum spending limits, which accomplish the same thing, but you don't have to prepay. In this way Paperspace is much more flexible to your changing GPU compute needs.
Most Jarvis users are running Jupyter notebooks in the Jarvis console. While vanilla Jupyter notebooks are useful (Paperspace has an option enable vanilla Jupyter), Gradient Notebooks from Paperspace offer a number of extended features related to GPU selection, data ingress/egress, and so forth. In other words, Paperspace has all the Jarvis functionality and much more.
Jarvis is designed to help spin-up a cloud GPU quickly and easily -- but that's a different concern than scale. Paperspace is designed to fit developers and teams from early prototyping stages all the way to production.
Although Jarvis Labs does a good job of onboarding new cloud GPU users into a simple-to-get-started Jupyter notebook, Paperspace adds a level of scalability and configuration that is only matched by the big cloud providers. Paperspace is advantaged by years of operating high-performance GPU data centers and a vertically integrated software stack that makes it easy to get something as simple as a GPU server running Ubuntu or as complicated as a private cluster for multi-GPU inference.
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Jupyter notebooks
"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!"
