Validated Hardware Stacks for Real AI Workloads

Starting from a blank slate when designing AI infrastructure is inefficient and risky. DVUN's reference architectures provide validated, real-world hardware stack designs for the most common AI infrastructure use cases — so you can start from a proven foundation and adapt to your specific requirements rather than designing from scratch.

Training Cluster Architectures

• 4-Node GPU Training Cluster — A compact, high-performance training cluster for teams moving off cloud. 4x 8-GPU servers, InfiniBand networking, shared NVMe storage. Suitable for fine-tuning and mid-scale training runs.
• 8-Node Distributed Training Cluster — A full-scale on-premise training cluster for foundation model work. 8x 8-GPU servers, InfiniBand fabric, high-throughput shared storage, and redundant networking.
• Single-Node Training Workstation — A 4-GPU workstation for individual researchers and small teams. Cost-effective entry point for owned compute.

Inference Deployment Architectures

• Single-Node Inference Server — A 2-4 GPU inference node for low-to-medium throughput production serving. Optimized for latency and cost per query.
• Inference Rack Pack — A full rack of inference-optimized GPU servers with high-speed networking for high-throughput production inference. Designed for GPU cloud and enterprise inference deployments.
• Edge Inference Node — A compact, power-efficient inference node for edge deployment. Suitable for robotics, industrial AI, and distributed inference applications.

Private Compute Node Architectures

• Startup Pod — A complete, pre-configured compute environment for AI startups. GPU server, networking, storage, and rack infrastructure in a single validated package.
• Private Node Kit — A sovereign compute node for organizations that need full hardware control. Configurable GPU, storage, and networking in a compact rack footprint.
• Mini Cluster Stack — A 4-node private cluster for organizations that need more than a single node but aren't ready for a full rack deployment.

GPU Cloud Infrastructure Architectures

• GPU Cloud Starter Rack — A complete rack-scale GPU cloud infrastructure for new cloud providers. High-density GPU servers, 400G network fabric, shared storage, and facility infrastructure.
• Multi-Rack GPU Cloud Expansion — A validated architecture for expanding an existing GPU cloud deployment. Designed for consistent performance and operational simplicity at scale.

Need a Custom Architecture?

Our reference architectures are starting points. Every deployment has unique requirements. Our Solution Design service creates a custom hardware architecture matched to your specific workload, space, power, and budget constraints.

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