INQUIRE Lab researchers use Devola, Surena, and Yorha for AI, sensing, simulation, and scientific computing. The systems are shared through Docker so students can move from coursework to serious experiments without rebuilding their environment every time.
Featured System
Yorha
Shared Workflow
Docker-first access
Reproducible environments, project-level isolation, and shared GPU use for student research.
Research GPU pool
3 systems
Devola, Surena, and Yorha anchor the lab's shared compute bench.
Access model
Docker
Students work in isolated, reproducible environments instead of fighting dependencies.
Growth path
Expanding
The GPU footprint is designed to grow with research demand.
Machine Atlas
Each facility entry is managed from the control panel, including the image, GPU profile, description, detail chips, sort order, and optional YouTube walkthrough link.
Mixed-GPU research workstation
GPU Profile
RTX 5090, RTX 3090, RTX 3080 Ti, and RTX 4080 SUPER class GPUs
Devola supports high-throughput experimentation across AI, sparse computing, simulation, and systems research. Its mixed GPU pool gives students practical access to several accelerator generations.
Professional high-memory GPU node
GPU Profile
NVIDIA RTX PRO 6000 accelerators
Surena is built for researchers who need professional GPUs for large models, high-memory workflows, visualization, and data-intensive scientific computing.
Six-GPU compute system
GPU Profile
Six installed GPUs, with additional expansion planned
Yorha gives the lab a dedicated six-GPU platform for parallel experiments, multi-user research jobs, and fast iteration on containerized workloads.
Shared Access Model
Students can work in project containers while the lab keeps the hardware centralized. That makes experiments easier to reproduce, compare, and continue across semesters.
Project workspace
Docker image
GPU assignment
Experiment run
Results review
GPU systems support AI, sensing, computer vision, simulation, security, and scientific computing work.
Docker keeps each project reproducible while allowing multiple students to share the same hardware safely.
Compute can be assigned by project, course, or researcher without requiring a dedicated machine for every student.
The facility is built around a growing GPU pool and a workflow that can scale as new accelerators arrive.
The facility supports mentoring, coursework, research prototypes, and publication-grade experiments. The photos stay faithful to the real equipment while the page gives the systems a cleaner editorial presentation.
Video Ready
Each system can include an optional YouTube URL from the control panel. When a link exists, the public page shows a video call-to-action for that facility.
Access
Capacity
Expansion
