For Universities & Research Labs

Cloud GPU Labs for Every Student.

Credit-based GPU access that scales with your semester — not a 3-year depreciation cycle. Give every student their own H100, A100, or L4 environment.

Calculate Your SemesterSee comparison
Trusted by universities & training programs

Trusted by: IIT Madras, CMU, VESIT, upGrad, 100xEngineers

University GPU Access: Three Approaches Compared

4 GPUs. 80 students. Everyone needs access at unpredictable times. The hardware is the easy part — building the software to schedule, isolate, and store each student's work is the problem no one budgets for.

Buy ServersTraditional CloudJarvisLabsBEST FIT
Upfront Cost$200K-500K+$0$0
Ongoing CostIT staff + power + coolingPer-hour, complex billingPer-minute, simple credits
Student AccessShared, scheduled slotsIndividual accounts (admin nightmare)Individual environments, instant
Cost ControlNone per studentManual budget alertsPer-student caps + auto-pause
GPU FlexibilityFixed hardwareAny GPU, complex setupAny GPU, one-click launch
MaintenanceFull IT team requiredCloud admin neededFully managed
Seasonal ScalingIdle during breaksPay only when usedPay only when used

How Credits Flow

Credits map to how university budgets already work: grants fund departments, departments fund labs, labs fund students.

University
Buys a credit pool
Departments
Receive allocation
Professors
Distribute to labs
Students
Get GPU environments
Per-student budget caps prevent overspending
Auto-pause when credits run low (not deleted)
No surprise bills, ever
Interactive Calculator

Calculate Your Semester

Enter your numbers. See exactly how credits flow to every student, and how it compares to alternatives.

~$28
per student per semester

Based on 4 hrs/week of L4 GPU over a 16-week semester. Adjust in the calculator above.

For IT Admins & Department Heads
Create teams and invite students easily
Set per-student credit caps to prevent overspending
Monitor usage and withdraw credits in real time
Consolidated billing with bulk purchase orders
Enterprise-grade security

For Professors: Quick Start

Credits are in your account. Here's how to get your class on GPUs in under 10 minutes.

1

Students create accounts

Free signup at jarvislabs.ai. No credit card required for students.

2

Credits assigned

Department admin allocates credits to each student from the pool.

3

Pick a GPU + environment

Choose from pre-configured stacks: PyTorch, TensorFlow, ComfyUI, or use a startup script to set up a custom environment for the whole class.

4

Start coding

JupyterLab, VS Code, or SSH. Instance auto-pauses when credits run low.

Frequently Asked Questions

Common questions from university IT teams, department heads, and professors.

How much does GPU access cost per student?
Cost depends on the GPU type and hours used. An L4 GPU costs $0.44/hr. For a typical 16-week semester at 4 hours/week, that’s roughly $28 per student. An A100 is $1.29/hr (~$83/student/semester), and an H100 is $2.69/hr (~$172/student/semester). Students can set an auto-pause timer (e.g. 8 hours) so instances stop automatically after that duration, preventing forgotten running instances from burning through credits.
Do students need a credit card to sign up?
No. Students create a free account at jarvislabs.ai — no credit card required. The university purchases a credit pool, and department admins allocate credits to each student from that pool.
What happens when a student runs out of credits?
Their instance is paused — not immediately deleted. Files and work are preserved, but storage charges continue to accumulate while paused. If credits are not topped up within a few days, the instance is eventually deleted. Admins can top up credits at any time to keep instances running. There are no surprise compute bills — only the predictable storage cost accrues while paused.
What GPUs are available for university use?
JarvisLabs offers a range of GPUs from entry-level to research-grade, including L4, A100, and H100. Students can pause their instance and resume it on a different GPU type — start with a smaller GPU for coursework and switch to a larger one when needed for research. Any GPU available on the platform can be used, and new GPU types are added as they become available.
What development environments do students get?
Each student gets their own isolated environment with JupyterLab, VS Code, or SSH access. Pre-configured stacks are available for PyTorch, TensorFlow, and ComfyUI. Professors can also use a startup script to set up a custom environment for the whole class.
How does billing work? Can we use a purchase order?
Yes. JarvisLabs supports consolidated billing with bulk purchase orders — no individual student billing or credit card management. The university buys a credit pool upfront, and admins distribute it across departments and students.
Can we control how much each student spends?
Yes. Admins set per-student credit caps. You can monitor usage in real time, withdraw credits from inactive students, and redistribute them. When a student’s credits run low, their instances are paused automatically — no risk of overspending.
What happens during semester breaks? Do we keep paying?
No. With JarvisLabs, you only pay for active compute time. During breaks, if no one is running instances, you pay nothing aside from storage costs for persisted environments. This is a key advantage over on-prem servers, which cost the same whether they’re idle or busy.
How quickly can we get started?
You can start a free pilot with trial credits — no procurement process or commitment required. Fill out the pilot form and we’ll set you up within 1–2 business days.
Is there a minimum purchase or commitment?
No minimum commitment. You can start with a free pilot, then scale to a full department or university-wide deployment. Credits don’t expire — they stay in your account until used. The only ongoing cost is storage for any persisted environments.
Free trial credits included

Start a Free Pilot

Get trial credits for your department. No procurement process. No commitment.

Or email us directly at sales@jarvislabs.ai