A fire CAD analytics VLAN team must grow training capacity. How should they evaluate scale-out versus scale-up GPU strategies?

Evaluating scaling out with additional GPU servers for data-parallel throughput on cost per GPU while aligning NVLink domains with fabric, power and rack capacity evaluates only the scale-out side; without comparing against scaling up denser multi-GPU nodes for parallelism and NVLink needs, it cannot determine when fat nodes would be more efficient for fire CAD training. That evaluation works because neither strategy is universally correct: some CAD models scale across nodes; others prefer fat NVLink islands; power, cooling, and spine bandwidth constrain both paths. DCAI compute scalability judgment is comparative analysis, not a slogan. Prioritizing scaling out across more GPU servers for tensor-parallel jobs needing NVLink locality while balancing fabric bandwidth against power and cooling limits misplaces tightly coupled shards off the high-bandwidth NVLink island onto slower inter-node fabric; tensor-parallel work prefers dense scale-up nodes, and fabric plus facilities must be compared across both paths, not within scale-out alone. Evaluating scale-out alone for data-parallel throughput on cost per GPU, even while aligning NVLink domains with fabric, power and rack capacity, omits the required scale-out versus scale-up comparison and fails to test how mixed CAD parallelism would behave on denser nodes. Expanding per-GPU HBM capacity to fit larger model batches onto fewer multi-GPU nodes while tuning NVLink topology with fabric, power and thermal headroom tunes only the scale-up path; without comparing to scaling out additional servers, it cannot choose the efficient growth plan across parallelism types and facilities limits.

Exam caveat: hybrid answers are normal—scale up to the NVLink sweet spot, then scale out—so "compare" may yield a sequenced plan rather than a single purchase. Operational check: for the fire CAD training job, measure scaling efficiency on denser nodes versus more nodes, overlay PDU/cooling and fabric headroom, and pick the path (or sequence) that hits accuracy/time goals within facilities budget.