📓 Notebooks & Dev
JupyterLab with CUDA ready
GPU notebook with PyTorch, Transformers and Diffusers ready
From
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cheapest machine that meets this template
- Video memory
- 8 GB
- Setup
- ~4 min
- Access
- port 8888
- Billing
- hourly, in BRL
A notebook environment with PyTorch and CUDA already installed and the card available. No session expiring mid-training and no queue to get a GPU.
Official Project Jupyter JupyterLab with CUDA 12, PyTorch 2.x, Transformers, Diffusers, Accelerate, BitsAndBytes pre-installed. Ideal for fine-tuning, prototyping, dataset exploration. Auth token generated automatically.
What it is for
- Train and evaluate models with no session limit
- Heavy data analysis with GPU acceleration
- Teaching and research with an identical environment for everyone
- Your data and notebooks on your own machine
How to deploy
- Create your account and add balance (card or Pix, no subscription).
- In the console, pick the JupyterLab CUDA template and a machine — the console hides the ones that do not meet the requirement.
- In about 4 minutes the setup finishes and the access address shows up in the panel, on port 8888.
Done? Just destroy the machine and billing stops with it. No contract, no minimum commitment.
FAQ
Which card should I pick?
8 GB handles experiments and light fine-tuning; serious training wants 24 GB or more.
Do files disappear when I stop the machine?
Not while the instance exists. Even so, keep a copy of anything important elsewhere.
Can I install my own libraries?
Yes, it is your machine: install whatever you need.
Run JupyterLab CUDA today
You only pay for the hours the machine is running.