Use cases
The GPU comes to
your work.
Every GPU cloud makes you move into their box. Tangible attaches an idle peer GPU to the tools you already use — here’s where that fits.
AI / ML development
Getting a notebook onto a cloud GPU means quota requests, an instance to provision, CUDA versions to match, and your project uploaded into their box.
Ask for a GPU from the Jupyter you already use. One click and it opens in your browser on a peer's card — torch.cuda.is_available() is true, billed by the minute, no setup.
Who uses it · ML engineers, data scientists, AI researchers, students
3D & rendering
A heavy Blender render crawls on a laptop, and a workstation that keeps up runs into the thousands — or a render farm priced for studios with monthly minimums.
Drop a Blender project. We read the frames from the file, split them across matched idle GPUs to render in parallel, and land the sequence back in your downloads.
Who uses it · Freelance 3D artists, indie studios, motion designers
Science & research
Shared HPC queues mean waiting hours for a slot, and a local GPU server costs $50K+ — for a job that’s really just Python on a GPU.
Run your training, inference or simulation notebook on a real CUDA GPU in minutes — no queue, no cluster login, no reserved hardware. Wiped when you disconnect.
Who uses it · Computational scientists, bioinformaticians, grad students
Earn from your GPU
That expensive gaming or workstation GPU sits idle 20 hours a day. Selling its time usually means clunky mining tools or crypto payouts you don’t want.
Flip a switch and rent your card out while you’re away. Jobs run sealed off in a sandbox, you’re paid in cash, and the moment you need your PC it stops — your work comes first.
Who uses it · Gamers, 3D artists, anyone with an idle GPU