Lambda Labs is known for selling physical computers which come with physical GPU cards. Recently Lambda has started to offer a GPU service in the cloud. The service offers a solid assortment of GPUs with high performance-to-price ratios but the service is extremely simple in its implementation, offering Jupyter notebook and a way to SSH into the machine.
Sometimes all you need is to spin-up a GPU server and SSH into it or run a Jupyter notebook -- Paperspace as well is great at these simple tasks. But when it comes to scaling, persisting data, issuing public IPs, and so forth, Paperspace exceeds Lambda's capabilities.
Lambda has some of the most popular deep learning-centric GPUs such as the NVIDIA RTX 6000, A6000, and V100 16 GB -- but the selection is fairly limited. By contrast, Paperspace has more than a dozen different kinds of GPUs with vastly more configuration options including multi-GPU instances, high-memory instances such as the V100 32 GB, and higher top-end machines such as the A100 80 GB available in 1x, 2x, 4x, and 8x configurations.
Paperspace is first and foremost a GPU cloud infrastructure provider. Lambda is primarily a hardware vendor. Although Lambda possesses legitimate expertise in the design and manufacture of GPU-backed computers -- the cloud product lags behind the hardware product.
In addition to virtual machines and GPU-backed servers, Paperspace also provides Gradient, which is a software stack for deep learning users to run notebooks, create workflows, and serve deployments. Paperspace provides a huge number of tools in the cloud to deep learning users that Lambda does not.
Lambda Labs is an excellent hardware provider for GPU users who need to run their machines flat-out 24/7 and have the budget to do so. Lambda Cloud is taking some of these learnings into the cloud to provide GPUs but the stability and quality of the service lags behind some competitors.
Meanwhile, Paperspace provides a wider selection of GPUs in the cloud with more data centers, more configuration options at the GPU, system, and administrative levels, and provides an excellent developer-first experience to help individuals and teams get up and running quickly in the cloud.
Check out the Ultimate Guide to GPU Cloud Providers! It's all there!
Or do you have a question about this comparison that isn't answered? Please let us know!
500K+
Users
100M+
Compute hours
1M+
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!"
