High Performance Labs
Ready-to-use GPU labs
for research and teaching.
Managed, reproducible GPU environments for universities, research teams, and enterprise R&D. Pre-configured PyTorch and Jupyter, multi-user access with governance, fractional GPUs so many users share efficiently, and per-second billing so labs cost only when used.
The problem
Teams that should be doing research or teaching end up running infrastructure instead.
Research and teaching teams burn weeks building and babysitting GPU environments instead of doing science or teaching.
Giving many students or researchers reproducible, governed access is painful - versions drift, setups break, and every user hits a different wall.
On-prem GPU clusters are capex-heavy and sit idle between projects and semesters, yet still cost money to power and maintain.
Access control and cost governance are hard. Who can use what, for how long, and on whose budget is rarely clear until the bill arrives.
How PodStack solves it
Hosted, reproducible labs that many people can share - and that only cost money in use.
Ready-to-use GPU labs with PyTorch, Jupyter, CUDA, and course or project templates baked in. Users open a browser and start working, not troubleshooting installs.
Every user gets the same known-good image, so results reproduce. Multi-user access with roles, quotas, and governance keeps a whole cohort or lab consistent.
PodVirt fractional GPUs let many users share A10G, L40S, A100, and H100 hardware, so a class of dozens runs without a GPU per head.
Per-second billing means an environment costs money while a student is training and nothing when the lab is idle overnight, on weekends, or between terms.
Run it managed on PodStack, or license the platform to self-host inside your own institution for data residency and full control.
Specs and details
Frequently asked questions
What is a High Performance Lab?+
It is a managed, hosted GPU environment your team or class can use out of the box. Instead of building and maintaining GPU machines yourself, users log in to pre-configured environments - PyTorch, Jupyter, and your own templates - on fractional or full NVIDIA GPUs, with multi-user access and governance handled for you.
How does it work for a whole class or research group?+
Many users share pooled GPU capacity through PodVirt fractional GPUs, each getting the same reproducible environment. Admins set roles, quotas, and budgets, so a cohort of dozens can work in parallel without provisioning a GPU per person or losing track of who is spending what.
Do we pay for idle time?+
No. Labs are billed per second and only cost money while a session is actually running. Environments left idle overnight, on weekends, or between semesters do not rack up GPU charges, which is what makes hosted labs far cheaper than idle on-prem clusters.
Can we host the labs ourselves?+
Yes. You can run labs managed on the PodStack cloud, or license the platform and self-host it inside your own institution when data residency or full control is required.
Talk to our team
Tell us who the lab is for, how many users, and what they need to run. We will design a managed or self-hosted lab and send back a quote.