Solution · Notebooks

Your notebook.
Someone else’s GPU.

Keep the Jupyter you already use. One click and its kernel runs on a peer’s idle GPU — the notebook opens in your browser, torch.cuda.is_available() is true, and nothing about how you work changes.

Get access

How it works

Four steps, zero terminal.

Pick a GPU

Choose how long and how much VRAM. We match a real, idle peer GPU that fits — no bidding, no boxes to configure.

One click

Press Start. The seller does nothing — we bring their GPU up and open JupyterLab in your browser, pointed at it.

Bring your data

%tangible upload streams your files straight into the session. %pip install just works.

Run, then it’s gone

Your cells run on the remote card, billed by the minute. When you close the tab, the whole workspace is wiped.

import torch torch.cuda.is_available() # True — on a peer’s card, through your own notebook %tangible upload # bring your dataset in; wiped when you’re done

Why it’s different

Every GPU cloud makes you move. We don’t.

	Vast / RunPod / Salad	Tangible
Where you work	Upload into their pod / notebook	Your own notebook, GPU attached to it
Setup	Provision a box, pick an image, SSH	One click, opens in your browser
Your data after	Sits on their disk until you delete it	RAM-only, wiped on session end
Payment	Card / credits / crypto	Cash, by the minute

Your data

The host never keeps your data.

Every session runs in an isolated sandbox with a RAM-backed workspace. Your files are decrypted only inside it, never written to the host’s disk, and destroyed the moment you disconnect. New pip installs stay isolated too — they can’t touch the seller’s machine.

Run your next notebook on a peer’s GPU.