Collection: Mini Cluster Stack

Distributed Training. Compact Footprint. No Compromises.

There'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.

The 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.

Every 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.

Why the Mini Cluster Stack Works

• True Distributed Training Capability: 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.
• Single-Rack Footprint: 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.
• Shared Storage for Multi-User Environments: Centralized NVMe storage node ensures all compute nodes access the same datasets and checkpoints, enabling multi-user scheduling and collaborative research workflows.
• Framework-Ready: Pre-validated for PyTorch DDP, DeepSpeed, Megatron-LM, and other distributed training frameworks. The networking configuration is documented for each framework's specific requirements.
• Expandable Beyond the Rack: When you outgrow the Mini Cluster Stack, the networking and storage architecture is designed to expand to additional racks without replacing the core infrastructure.
• Deployment in Days: 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.

From Single Node to Cluster: The Growth Path

Many teams start with the Startup Pod — 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 Private Node Kit and Lab Pod 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.

What's Included

• 2–4x GPU servers (8 GPU each, RoCEv2-capable NICs included)
• 1x High-radix 100GbE switch with RoCEv2 and PFC support
• 1x Shared NVMe storage node with 100GbE connectivity
• 1x 42U or 48U rack enclosure with rails and cable management
• 2x Intelligent PDUs with power monitoring
• All interconnect cables (DAC/AOC) pre-selected and labeled
• Distributed training configuration guides (PyTorch DDP, DeepSpeed)
• DVUN advisory support for cluster deployment and initial configuration

Small Cluster. Big Ambitions.

The 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. Request a quote for Mini Cluster Stack configurations, or contact our team to discuss the right node count and GPU configuration for your specific training workloads.