A GPU notebook, by the hour
Most experiments do not need a cluster — they need one good card for an afternoon. Start JupyterLab, do the work, stop the pod.
Jupyter, VSCode or RStudio in under two minutes
All three are official templates with the CUDA stack already in place. There is no install step before the first cell runs.
More than a Python kernel
RAPIDS for GPU dataframes, Polyglot for R and Julia beside Python, Bioconductor for genomics, Pangeo for climate.
It grows into the real run
When the experiment outgrows the notebook, move the volume to a bigger card. Nothing else about the setup changes.
Templates for this work
Official images with the drivers and the stack already in place.
- Jupyter
- VSCode
- RStudio
- RAPIDS Notebooks
- Scientific Python
- Polyglot Data Science
- Bioconductor
Cards that suit this work
Every Meshive pod gets a whole, non-virtualized GPU. These are the ones we would reach for first.
- RTX 309024GB at the price of a coffee per hour
- RTX 4090Fast enough that you stop waiting on cells
- RTX 509032GB when the dataset outgrows the rest
- RTX A5000Steady workstation card for long sessions
Three steps
- 1Pick Jupyter, VSCode or RStudio and launch it.
- 2Attach a volume so notebooks and data survive the pod.
- 3Open it in the browser. Move to a bigger card when the work needs one.
Also on Meshive
Pick a card and start the pod
Sign up, choose the GPU, and the pod is yours in under two minutes. It bills by the hour and stops when you stop it.
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