Collection: Lab Pod

Research Moves Fast. Your Infrastructure Should Too.

University AI labs and research teams operate under a unique set of constraints that commercial AI infrastructure vendors rarely understand. Grant timelines are fixed and unforgiving. Procurement processes are slow and bureaucratic. The team is composed of researchers who are world-class at AI but have limited time and interest in infrastructure management. And the workloads are diverse — multiple researchers running different experiments simultaneously, with different compute requirements, different datasets, and different software environments.

The Lab Pod was designed with these realities in mind. It's a pre-configured AI training cluster built specifically for the multi-user, multi-experiment environment of a research lab: enough GPU density to run serious training jobs, shared storage that all users can access simultaneously, networking that doesn't become a bottleneck when multiple experiments are running in parallel, and a deployment process that doesn't require a dedicated infrastructure team to execute.

Whether you've just received a compute grant and need to deploy quickly, or you're upgrading an aging lab cluster that can no longer keep pace with your team's ambitions, the Lab Pod gives you a validated, deployable AI training infrastructure that your researchers can be productive on within days of delivery.

Designed for the Research Lab Environment

• Multi-User GPU Scheduling: The Lab Pod's compute configuration is compatible with SLURM, PBS, and other HPC job schedulers that research labs use to share GPU resources across multiple users and projects.
• Shared Storage Architecture: Centralized NVMe storage node with high-speed network connectivity ensures all compute nodes can access shared datasets and model checkpoints simultaneously without storage becoming a bottleneck.
• Containerized Workload Support: Pre-validated for Docker and Singularity container environments, allowing researchers to maintain isolated software environments without infrastructure conflicts.
• Expandable Compute: The Lab Pod's networking and storage are sized to support additional compute nodes as your lab's needs grow — without requiring a full infrastructure rebuild.
• Low Administrative Overhead: Designed to be managed by a part-time system administrator or a technically capable graduate student, not a dedicated infrastructure team.
• Academic Procurement Friendly: DVUN works with academic procurement processes and can provide the documentation, quotes, and vendor information required for institutional purchasing workflows.

A Typical Lab Pod Deployment

A mid-sized AI research lab with 8–15 active researchers typically needs 32–64 GPUs of shared compute capacity, 100–500TB of shared storage, and 100GbE networking between compute and storage nodes. The Lab Pod is configured around this profile, with options to scale up or down based on your lab's specific size and workload mix. The deployment process is documented step-by-step, and DVUN's advisory team is available to support your system administrator through the initial setup. See our Private Node Kit for enterprise-grade private compute options, and our AI Storage Nodes for standalone shared storage solutions.

What's Included in the Lab Pod

• 2–4x GPU servers (8 GPU each, total 16–32 GPUs depending on configuration)
• 1x Shared AI storage node with 100GbE connectivity
• 1x High-radix 100GbE switch for cluster networking
• 1x Management server for job scheduling and cluster administration
• 1x 42U rack enclosure with rails, cable management, and PDU
• All interconnect cables pre-selected and labeled
• SLURM configuration guide and deployment documentation
• DVUN advisory support for initial deployment

Your Lab's Next Breakthrough Starts with the Right Infrastructure

Research doesn't wait for infrastructure. The Lab Pod gives your team the compute capacity to run the experiments that matter, on a timeline that fits your grant cycle, with the reliability that serious research demands. Request a quote for Lab Pod configurations, or contact our team to discuss customization for your lab's specific research focus and team size.