Inference emphasizes latency, concurrency and KV cache; fine-tuning adds optimizer or adapter state; full training adds activation, gradient and checkpoint pressure. Those profiles produce different hardware requirements. To address “Define the workload contract”, write down model, precision, context length, concurrency or batch target, latency objective and daily data movement before selecting components. Test that result on NVIDIA AI Server Hardware.

A workload that cannot fit its required weights and runtime state has a hard capacity problem, while a workload that fits but misses throughput has a scaling problem. To address “Size accelerator memory before accelerator count”, calculate the single-request or single-rank memory footprint first, then decide whether tensor, pipeline, data or expert parallelism is actually required. Test that result on NVIDIA AI Server Hardware.

System RAM holds the OS, orchestration, preprocessing, offload buffers, page cache and sometimes model or KV-cache spill. Starving the host can make an expensive GPU configuration unstable. To address “Leave CPU memory for the host”, measure host memory during a representative run and reserve capacity for services, peaks and failure recovery rather than matching RAM only to model size. Test that result on NVIDIA AI Server Hardware.

Multiple GPUs, NVMe drives and fast NICs can compete for CPU lanes or traverse switches with shared upstream bandwidth. Slot shape alone does not prove full electrical bandwidth. To address “Audit PCIe lane topology”, use the motherboard or server block diagram to map every GPU, NIC and storage device to roots, switches and NUMA nodes before ordering. Test that result on NVIDIA AI Server Hardware.

Boot, model repository, local cache, dataset staging, checkpoint output and log retention need different capacity, latency and endurance characteristics. To address “Give storage a workload role”, separate these tiers in the design and calculate peak read/write bursts rather than buying one undifferentiated SSD pool. Test that result on NVIDIA AI Server Hardware.

A single inference server may need modest east-west traffic, while multi-node training can be dominated by collectives. Storage traffic can also share or compete with the compute fabric. To address “Match network to distributed behavior”, estimate bytes exchanged per step or request and validate the topology with NCCL or application-level measurements at the planned node count. Test that result on NVIDIA AI Server Hardware.

High-power GPUs can require specific slot spacing, retention hardware, airflow direction, cable routing or liquid-cooling options. Consumer and workstation cards may not be supported in server chassis. To address “Verify chassis support, not just component fit”, check the exact OEM compatibility list, mechanical drawing and power-cable requirements for the selected accelerator and chassis. Test that result on NVIDIA AI Server Hardware.

GPU power is only part of the load. CPUs, memory, NICs, NVMe, fans, pumps and conversion losses add to the server draw and influence PSU redundancy. To address “Calculate power from the complete system”, use component limits for a screening total, then replace it with OEM nameplate and measured rack data before electrical design. Test that result on NVIDIA AI Server Hardware.

AI jobs can keep accelerators near high utilization for long periods, exposing cooling designs that survive short bursts but not continuous operation. To address “Treat cooling as a sustained-load problem”, validate inlet temperature, fan or pump behavior, throttling and room or liquid-loop capacity during a long representative stress test. Test that result on NVIDIA AI Server Hardware.

BMC access, firmware management, GPU telemetry, network counters, storage health and environmental sensors are essential when the system becomes production infrastructure. To address “Design management and observability early”, define what will be monitored, retained and alerted before deployment so capacity problems are visible before users report them. Test that result on NVIDIA AI Server Hardware.