{"title":"Lab Pod","description":"\u003ch2\u003eResearch Moves Fast. Your Infrastructure Should Too.\u003c\/h2\u003e\n\n\u003cp\u003eUniversity 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.\u003c\/p\u003e\n\n\u003cp\u003eThe 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.\u003c\/p\u003e\n\n\u003cp\u003eWhether 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.\u003c\/p\u003e\n\n\u003ch3\u003eDesigned for the Research Lab Environment\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eMulti-User GPU Scheduling:\u003c\/strong\u003e 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.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eShared Storage Architecture:\u003c\/strong\u003e 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.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eContainerized Workload Support:\u003c\/strong\u003e Pre-validated for Docker and Singularity container environments, allowing researchers to maintain isolated software environments without infrastructure conflicts.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eExpandable Compute:\u003c\/strong\u003e 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.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eLow Administrative Overhead:\u003c\/strong\u003e Designed to be managed by a part-time system administrator or a technically capable graduate student, not a dedicated infrastructure team.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eAcademic Procurement Friendly:\u003c\/strong\u003e DVUN works with academic procurement processes and can provide the documentation, quotes, and vendor information required for institutional purchasing workflows.\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch3\u003eA Typical Lab Pod Deployment\u003c\/h3\u003e\n\u003cp\u003eA 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 \u003ca href=\"\/collections\/private-node-kit\"\u003ePrivate Node Kit\u003c\/a\u003e for enterprise-grade private compute options, and our \u003ca href=\"\/collections\/ai-storage-nodes\"\u003eAI Storage Nodes\u003c\/a\u003e for standalone shared storage solutions.\u003c\/p\u003e\n\n\u003ch3\u003eWhat's Included in the Lab Pod\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e2–4x GPU servers (8 GPU each, total 16–32 GPUs depending on configuration)\u003c\/li\u003e\n\u003cli\u003e1x Shared AI storage node with 100GbE connectivity\u003c\/li\u003e\n\u003cli\u003e1x High-radix 100GbE switch for cluster networking\u003c\/li\u003e\n\u003cli\u003e1x Management server for job scheduling and cluster administration\u003c\/li\u003e\n\u003cli\u003e1x 42U rack enclosure with rails, cable management, and PDU\u003c\/li\u003e\n\u003cli\u003eAll interconnect cables pre-selected and labeled\u003c\/li\u003e\n\u003cli\u003eSLURM configuration guide and deployment documentation\u003c\/li\u003e\n\u003cli\u003eDVUN advisory support for initial deployment\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch3\u003eYour Lab's Next Breakthrough Starts with the Right Infrastructure\u003c\/h3\u003e\n\u003cp\u003eResearch 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. \u003ca href=\"\/pages\/request-a-quote\"\u003eRequest a quote\u003c\/a\u003e for Lab Pod configurations, or contact our team to discuss customization for your lab's specific research focus and team size.\u003c\/p\u003e","products":[],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0671\/0525\/9582\/collections\/lab-pod.png?v=1782105216","url":"https:\/\/dvun.com\/collections\/lab-pod.oembed","provider":"DVUN","version":"1.0","type":"link"}