Workload First
We start with the real workload: AI development, inference, analytics, visualization, rendering, simulation, media, or mixed compute.
Design before parts
GPU infrastructure should be engineered before ordering.
A GPU system is not just a video card in a box. Power, thermals, CPU lanes, memory, storage, networking, chassis design, software role, and service access all matter.
Platform Balance
CPU, GPU, memory, PCIe lanes, storage, networking, and expansion are reviewed as one complete system.
Power & Cooling
GPU density creates heat and power challenges. Chassis, airflow, PSU capacity, and rack environment are part of the design.
Analytics Integration
For AI, LPR, FR, smoke, fire, weapon detection, and other detection modules, the analytics server is sized around camera count, stream load, software requirements, and storage/network handoff.
Storage & Network
Datasets, video streams, scratch space, output files, backups, and network movement are planned with the compute platform.
Remote Management
Remote management, firmware planning, monitoring approach, and recovery access are part of the discussion.