The main alternatives for renting GPUs to train or run AI models fall into three groups: browser workspaces you open and code in, small on-demand GPU renters you drive from a terminal or your own code, and larger AI clouds with reserved capacity. Decide first whether you want a notebook or a machine you manage, then compare.
Paperspace's home page now sits on DigitalOcean, selling cloud GPUs for AI and machine learning work. If you came to it for a quick place to open a notebook and start coding, that shift may be exactly why you're reading this.
There's no shortage of places to rent a GPU. The harder part is finding one that matches how you like to work. The nine options below are in no particular order and grouped by working style, because that's the choice that shapes your week far more than the name on the invoice.
Notebook in a browser or a machine you run?
Here's what trips people up. Plenty of people who used a click-and-code workspace assume every GPU cloud works the same way. Most of the smaller ones don't. They hand you an instance, a command line or an API, and the rest is up to you.
Massed Compute's page says you can launch GPUs from your code. Jarvislabs says you drive your GPUs from the terminal. Thunder Compute sells persistent GPU instances for developers, alongside sandboxes for AI agents. All three are fine choices if you're comfortable setting up your own environment, installing libraries and shutting machines down when you've finished.
Only two tools here are built around a workspace you open in the browser. Lightning AI is an AI cloud where you build, train and deploy models with GPUs on tap. Deepnote is a collaborative notebook for Python and SQL, and it's worth being plain about it: it's a notebook for data work, not a GPU renter, so it suits analysis and sharing more than heavy model training.
So decide which style you want before you compare anything else. Moving from a notebook to a bare machine, or the other way round, is a change of working habit, not just a change of supplier. If your team has never looked after a server, that change will slow you down for weeks.
Three kinds of GPU home for your models
Browser workspaces and notebooks. The two tools at the end of the last section. They're the gentlest move if your habit is to open a tab, pick a GPU and start writing code straight away.
Small on-demand renters. The terminal-first renters from the last section sit here, along with Hyperstack, which pairs on-demand GPUs with an AI studio and, its page says, a solutions engineer you can talk to. These suit individual developers and small teams who want a GPU for a few hours or days without signing a contract.
Larger AI clouds with reserved capacity. Voltage Park offers on-demand and reserved NVIDIA capacity across several data centres and is built for demanding workloads, not hobby projects. Verda describes itself as a full-stack AI cloud covering compute, storage, networking and inference. Vultr Cloud GPU is a general cloud that also rents GPU machines, from single virtual machines to large clusters. Be realistic here: the first two suit teams with a developer who's happy to look after infrastructure.
The nine options side by side
| Tool | Kind of service | Good for |
|---|---|---|
| Deepnote | Collaborative data notebook | Python and SQL analysis shared with a team |
| Hyperstack | On-demand cloud GPUs with an AI studio | Running AI workloads quickly with human help on offer |
| Jarvislabs | On-demand GPU rental for practitioners | Renting high-end NVIDIA GPUs by the minute from a terminal |
| Lightning AI | Browser AI workspace with GPUs | Building, training and deploying models in one workspace |
| Massed Compute | Hourly GPU rental, no contracts | GPUs with CUDA ready, launched from your own code |
| Thunder Compute | Developer GPU instances and sandboxes | Persistent GPU instances, plus sandboxes for AI agents |
| Verda | Full-stack AI cloud | Compute, storage, networking and inference from one provider |
| Voltage Park | Reserved and on-demand GPU capacity | Demanding AI workloads that need capacity held for them |
| Vultr Cloud GPU | General cloud with GPU machines | AMD or NVIDIA GPUs alongside a general cloud |
Who each GPU cloud is for
Alphabetical, in no particular order.
Deepnote
Best for: Data teams who want a collaborative notebook for Python and SQL, now open source by its own account; it's a data notebook, not a place to rent GPUs.
Hyperstack
Best for: Small teams who want on-demand GPUs for AI and ML work in minutes, with an AI studio and a solutions engineer on offer (vendor claim).
Jarvislabs
Best for: Practitioners at home on a command line who want NVIDIA GPUs for training and inference, then turn one GPU into an endpoint on the same platform.
Lightning AI
Best for: PyTorch developers who'd like a workspace in the browser to build, train and deploy models, with on-demand GPUs, clusters and AI coding agents (vendor wording).
Massed Compute
Best for: Developers who want hourly NVIDIA GPUs with CUDA pre-installed, no contract, and instances they can start from their own code (vendor claim).
Thunder Compute
Best for: Developers who'd like a persistent GPU instance of their own, with separate CPU and GPU sandboxes for AI agents and fine-grained billing.
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Verda
Best for: Teams with a developer who want one AI cloud for compute, storage and networking, plus an inference service and confidential computing (vendor claim).
Voltage Park
Best for: Teams running demanding AI work that want NVIDIA capacity reserved or on demand across several data centres, rather than a GPU for a hobby project.
Vultr Cloud GPU
Best for: Businesses already on a general cloud that want AMD or NVIDIA GPUs too, from single virtual machines to large dedicated clusters and reserved capacity.
My advice for moving your GPU work
Start with how you work, not with the GPU model on the banner. If you live in notebooks and share results with colleagues, try the browser workspaces first. If you're a developer who already scripts your environment, the small renters give you a GPU with very little ceremony. If you're training at scale and need capacity held for you, talk to the larger clouds and ask what reserved capacity means in their contract.
Whichever way you go, keep your work portable: store notebooks in Git, keep datasets in storage you can move, and write down every library version. That way the next move, if there is one, takes an afternoon. My wider list of cloud GPU providers covers the bigger names, and if you're still working out what kind of hardware your job needs, my explainer on CPU versus GPU is a quick read.
Your first week on a new GPU cloud
- Day one: create an account, start the smallest GPU on offer and run a short script you already know works.
- Day two: rebuild your environment from a written list of libraries, not from memory.
- Day three: move one real dataset across and time how long it takes.
- Day four: run a training or inference job you've run before and compare the output.
- Day five: shut everything down and check nothing is still running in the background.
If that week feels like a fight, it's telling you the working style is wrong for you, whatever the hardware.
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Frequently asked questions
What's the difference between a GPU cloud and a notebook platform?
A GPU cloud rents you a machine with a graphics card, and you set up the software. A notebook platform gives you a ready coding workspace in the browser, with compute behind it.
Do I need a GPU to train a machine learning model?
Not always. Small models and data analysis run fine on a CPU. GPUs matter when you're training deep learning models or running large models quickly.
Can I move my notebooks between platforms?
Usually, yes. Jupyter-style notebooks are files you can download and open elsewhere. The work is in rebuilding the environment, so keep a list of the libraries and versions you use.
What does reserved GPU capacity mean?
It means a provider holds a set amount of GPU hardware for you over an agreed period, so it's there when you need it. On-demand means you take what's free at the time.
Are small GPU clouds safe for company data?
It depends on the provider and your own rules. Ask where data is stored, who can reach it and how it's deleted, and check what your data protection regulator requires.
Should a beginner rent a bare GPU machine?
Only if you're happy at a command line. A browser workspace is a gentler start, and you can move to a bare machine once you know what you need.
