{"title":"Mini Cluster Stack","description":"\u003ch2\u003eDistributed Training. Compact Footprint. No Compromises.\u003c\/h2\u003e\n\n\u003cp\u003eThere's a compute threshold where single-node training stops being practical. The model is too large to fit in the VRAM of a single server. The dataset is too big to process in a reasonable time on one machine. The team has grown to the point where multiple researchers need GPU access simultaneously. When you hit this threshold, you need distributed training infrastructure — but you don't necessarily need a 20-rack data center to get it.\u003c\/p\u003e\n\n\u003cp\u003eThe Mini Cluster Stack is DVUN's compact multi-node training cluster: a 2–4 node distributed training environment that fits in a single rack, deploys in a single week, and delivers the distributed training capabilities that growing AI teams need without the complexity, cost, and footprint of a full-scale cluster deployment. It's the natural next step after the Startup Pod, and the right foundation for teams that are serious about distributed training but not yet ready for a full private AI cloud buildout.\u003c\/p\u003e\n\n\u003cp\u003eEvery component in the Mini Cluster Stack has been selected and validated for distributed training performance: GPU servers with the right interconnect topology for collective operations, a high-bandwidth low-latency switch fabric for efficient gradient synchronization, shared storage with the throughput to feed multiple training nodes simultaneously, and the power and cooling infrastructure to run it all reliably in a single rack.\u003c\/p\u003e\n\n\u003ch3\u003eWhy the Mini Cluster Stack Works\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eTrue Distributed Training Capability:\u003c\/strong\u003e Not just multiple servers sharing a switch — a properly designed cluster with RoCEv2-capable networking, RDMA-enabled NICs, and the switch configuration to support efficient all-to-all collective operations.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSingle-Rack Footprint:\u003c\/strong\u003e Everything fits in one 42U or 48U rack enclosure. No multi-rack coordination, no complex inter-rack cabling, no raised floor requirements. Deploy in a server room, a large office, or a co-location cage.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eShared Storage for Multi-User Environments:\u003c\/strong\u003e Centralized NVMe storage node ensures all compute nodes access the same datasets and checkpoints, enabling multi-user scheduling and collaborative research workflows.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eFramework-Ready:\u003c\/strong\u003e Pre-validated for PyTorch DDP, DeepSpeed, Megatron-LM, and other distributed training frameworks. The networking configuration is documented for each framework's specific requirements.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eExpandable Beyond the Rack:\u003c\/strong\u003e When you outgrow the Mini Cluster Stack, the networking and storage architecture is designed to expand to additional racks without replacing the core infrastructure.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDeployment in Days:\u003c\/strong\u003e Detailed rack layout diagrams, network configuration guides, and DVUN advisory support mean your team can go from hardware delivery to first distributed training job in under a week.\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch3\u003eFrom Single Node to Cluster: The Growth Path\u003c\/h3\u003e\n\u003cp\u003eMany teams start with the \u003ca href=\"\/collections\/startup-pod\"\u003eStartup Pod\u003c\/a\u003e — a single GPU server for initial training and inference workloads. As the team grows and the models get larger, the Mini Cluster Stack is the natural next step: 2–4 nodes with shared storage and proper cluster networking, in the same rack footprint. And when the Mini Cluster Stack is no longer enough, the \u003ca href=\"\/collections\/private-node-kit\"\u003ePrivate Node Kit\u003c\/a\u003e and \u003ca href=\"\/collections\/lab-pod\"\u003eLab Pod\u003c\/a\u003e provide the path to full-scale private AI infrastructure. DVUN's Ready Systems are designed as a growth ladder — each step building on the last.\u003c\/p\u003e\n\n\u003ch3\u003eWhat's Included\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e2–4x GPU servers (8 GPU each, RoCEv2-capable NICs included)\u003c\/li\u003e\n\u003cli\u003e1x High-radix 100GbE switch with RoCEv2 and PFC support\u003c\/li\u003e\n\u003cli\u003e1x Shared NVMe storage node with 100GbE connectivity\u003c\/li\u003e\n\u003cli\u003e1x 42U or 48U rack enclosure with rails and cable management\u003c\/li\u003e\n\u003cli\u003e2x Intelligent PDUs with power monitoring\u003c\/li\u003e\n\u003cli\u003eAll interconnect cables (DAC\/AOC) pre-selected and labeled\u003c\/li\u003e\n\u003cli\u003eDistributed training configuration guides (PyTorch DDP, DeepSpeed)\u003c\/li\u003e\n\u003cli\u003eDVUN advisory support for cluster deployment and initial configuration\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch3\u003eSmall Cluster. Big Ambitions.\u003c\/h3\u003e\n\u003cp\u003eThe Mini Cluster Stack is for teams that are serious about distributed training but smart about infrastructure investment. You get the distributed training capabilities you need today, in a footprint and at a cost that makes sense for where you are now — with a clear path to scale when you're ready. \u003ca href=\"\/pages\/request-a-quote\"\u003eRequest a quote\u003c\/a\u003e for Mini Cluster Stack configurations, or contact our team to discuss the right node count and GPU configuration for your specific training workloads.\u003c\/p\u003e","products":[],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0671\/0525\/9582\/collections\/mini-cluster-stack.png?v=1782105313","url":"https:\/\/dvun.com\/collections\/mini-cluster-stack.oembed","provider":"DVUN","version":"1.0","type":"link"}