An infrastructure engineer is configuring an orchestrator for an AI training cluster containing a mix of NVIDIA H100, A100, and T4 GPUs. To maximize overall resource utilization efficiency and throughput for concurrent jobs, how should the orchestrator assign workloads to the different GPU resources?
Select an answer to reveal the explanation.
Short Explanation
Think of it like this: if you need a gallon of milk, you don't rent a semi-truck; if you're moving your whole house, you do. Match the workload to the GPU horsepower: put massive training runs on H100s and let T4s handle smaller inference or simple transfer learning. That's how you keep the cluster efficient and stop wasting expensive compute.
Full Explanation
Managing a heterogeneous AI cluster—one that contains various generations and tiers of GPUs—requires intelligent workload scheduling to maximize resource efficiency, reduce job completion times, and minimize costs. Different AI workloads have distinct hardware requirements: training large language models requires massive FP8/FP16 compute power and high-speed GPU-to-GPU interconnects like NVLink, while simple inference tasks, model evaluation, or light model tuning require far fewer resources.
An efficient scheduling strategy matches the compute intensity and memory requirements of a job with the capabilities of the GPU. Deploying massive, compute-heavy foundation model training jobs on top-tier accelerators like the NVIDIA H100 or A100 ensures they complete quickly and utilize high-bandwidth memory such as HBM3 or HBM2e effectively. Conversely, routing lighter tasks such as small-scale inference or simple transfer learning to entry-level GPUs like the NVIDIA T4 or L4 prevents resource waste and keeps high-end accelerators free for demanding workloads.
Assigning all incoming jobs to the H100 nodes exclusively until they run out of memory is inefficient because it underutilizes the A100 and T4 GPUs, creating a severe bottleneck on the H100 nodes while other expensive hardware remains idle.
Grouping workloads based on their physical network proximity to the cloud storage bucket location, while ignoring the GPU architecture, is wrong because data locality is only one factor. Scheduling based solely on proximity ignores the fact that a T4 cannot run a massive large language model training job due to memory limitations, leading to out-of-memory errors or extreme performance penalties.
Dedicating the entire cluster's resources to one job at a time in a strict first-in, first-out queue regardless of size leads to extremely poor throughput. Small jobs would be blocked waiting for massive jobs to finish, and the cluster's GPUs would be under-utilized during phases of the job that do not scale to the entire cluster.
Matching compute intensity to GPU capability ensures the right resource is used for the right job, maximizing overall cluster efficiency.