Buy the Right Hardware the First Time

AI hardware procurement is full of traps: overpaying for FLOPS you don't need, buying networking that bottlenecks your GPUs, or selecting storage that starves your training pipeline. DVUN's buying guides cut through the spec sheet noise and give you practical, workload-specific guidance for making the right hardware decisions.

GPU & Accelerator Buying Guides

• Training vs. Inference: Choosing the Right GPU — The GPU that's optimal for training is often wrong for inference. Understand the key differences and how to select accordingly.
• NVIDIA GPU Comparison: H100 vs. A100 vs. L40S — When each generation makes sense, and when the price premium is (and isn't) justified.
• Multi-GPU Server Configurations — 2-GPU vs. 4-GPU vs. 8-GPU: understanding NVLink, PCIe bandwidth, and when more GPUs per node is the right call.
• Accelerator Cards for Edge AI — Selecting the right accelerator for edge deployment: power envelope, form factor, and inference performance.

Networking Buying Guides

• InfiniBand vs. Ethernet for AI Clusters — When InfiniBand's latency advantage justifies the cost, and when 100G/400G Ethernet is the right choice.
• Selecting NICs for AI Workloads — RDMA, RoCE, and what actually matters for your cluster interconnect.
• Switch Selection for GPU Clusters — Port counts, oversubscription ratios, and how to size your network fabric correctly.

Storage Buying Guides

• NVMe Storage for AI Training — Sequential read performance, endurance ratings, and how to size local storage for your training pipeline.
• Shared Storage for Multi-Node Clusters — When local NVMe isn't enough and how to select the right shared storage architecture.
• Memory Configuration for GPU Servers — DRAM sizing, memory bandwidth, and how system memory affects GPU utilization.

Need a Custom Recommendation?

Our buying guides cover the most common scenarios. If your use case is more specific, our team can provide a personalized hardware recommendation based on your exact workload and budget.

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