AI Infrastructure by Workload

Inference and Application Services

For model serving, document the model size, precision, context length, concurrent requests and target latency or throughput. Some applications can run on a single GPU; others need model partitioning or multiple replicas.

PCIe systems with L4, L40S or professional RTX PRO Blackwell Server Edition GPUs offer configurations to evaluate for smaller enterprise deployments. Validate the exact application and supported GPU count on the selected OEM platform.

Fine-Tuning and Distributed Training

Specify whether the project updates an existing model or trains a model across multiple GPUs. Record the framework, precision, optimizer, dataset and checkpointing requirements. GPU memory and communication topology both matter to system selection.

Eight-GPU HGX and DGX platforms are options to evaluate when work is distributed within a node. For multiple nodes, the server selection must be accompanied by an explicit fabric and storage design.

Scientific and AMD Accelerator Workloads

Scientific compute and research procurement should start with the actual application, supported accelerator backend and software versions. Confirm the required libraries, numerical precision and validation process.

AMD Instinct builds provide an additional platform to evaluate where the application supports the selected ROCm environment. Existing CUDA deployments should be reviewed for portability before a hardware change.

Define the Deployment Scale

Scenario fit is a starting point for evaluation. Catalog entries do not establish measured application performance; compare configurations using a representative workload.