Start from the learning outcomes, not the hardware
Define what a graduate of the lab should be able to do: prepare a dataset, train and evaluate a model, optimise it for an edge device, and integrate it into a physical system. Every purchase after that is justified by an outcome.
In practice we map three tiers of work: foundation (Python, data, classical machine learning), applied (computer vision, natural language, generative models) and integration (edge deployment, robotics, IoT).
- Foundation tier: data handling, classical ML, model evaluation.
- Applied tier: vision, speech and language projects with real datasets.
- Integration tier: edge inference, robotics and IoT capstone projects.
Core equipment of an AI lab
A balanced lab combines shared compute with individual stations and a small fleet of edge and robotic devices so that models leave the screen.
| Layer | Typical equipment | Teaching role |
|---|---|---|
| Shared compute | GPU server or workstation cluster | Training and fine-tuning larger models |
| Student stations | Workstations with dedicated GPUs | Daily coursework, notebooks, projects |
| Edge AI | Embedded AI kits and vision cameras | Deploying optimised models outside the server |
| Robotics | Desktop robotic arms and mobile ROS platforms | Perception, control and integration projects |
Room layout, power and network
Group stations in pods of four to six so instructors can move between teams. Keep the robotics zone away from the wall of workstations and give it clear floor space and safety marking.
Plan dedicated power circuits for the GPU server, a UPS for the compute rack, and a gigabit local network segment so datasets move quickly between the server and the stations.
Curriculum, training and handover
Hardware without a syllabus ages quickly. Each GateIn lab is delivered with lesson plans, project briefs and instructor training so the faculty can run the lab independently from the first semester.
Commissioning includes installation, software imaging, an acceptance test for every device, and a handover session covering maintenance and consumables.
Common questions
How much space does a university AI lab need?
A lab for 20 to 25 students typically needs 60 to 90 square metres, including a separate robotics and testing zone.
Can the lab be delivered in phases?
Yes. Most institutions start with student stations and edge kits, then add shared GPU compute and robotics platforms in a second phase.