Selecting a Complete AI Server

Define the Workload First

Record the model family, precision, context length, expected concurrency and whether the project involves inference, fine-tuning or distributed training. Include the framework and software versions. These inputs make configurations comparable and give the evaluation a defined target.

PCIe Servers for Smaller Deployments

A configured PCIe server is an option to evaluate for inference, development and applications that can run on individual GPUs. Confirm supported GPU count, CPUs, system memory, storage, network interfaces, power supplies and cooling for the exact OEM platform.

For example, NVIDIA L40S provides 48 GB of GPU memory and PCIe connectivity, without NVLink support. Several installed L40S GPUs do not create the same interconnect topology as an HGX platform. NVIDIA L40S specifications.

HGX for Work Across Multiple GPUs

Evaluate HGX when the application benefits from GPUs exchanging data within a node. NVIDIA HGX H100, H200 and B200 platforms use eight GPUs with NVLink and NVSwitch connectivity. Training and inference can both use these platforms; fit depends on memory, software behavior and communication requirements. NVIDIA HGX architecture.

Integrated Racks and Project Scope

A rack-scale purchase extends beyond an individual server. GB300 NVL72 connects 72 GPUs in an NVLink domain and uses liquid cooling. Confirm the exact rack, cooling equipment and facility interfaces before comparing offers. Cisco / Supermicro GB300 documentation.

Request installed components, software entitlements, service coverage, delivery terms and exclusions in each offer. Separate facility work and external fabric from hardware cost. The scenario guidance here is a planning starting point; validate important assumptions with a representative application test.