GPU Servers and HPC Infrastructure Consulting Services

GPU and high-performance computing handle computationally intensive tasks, such as training and deploying AI models, simulations, rendering, and data-intensive analysis. These workloads operate very differently from standard office computing.

Scope

• Training workloads: creating new models, which require sustained, high-throughput compute power for periods ranging from hours to days.
• Inference workloads: operating a model on a daily basis, where cost-per-request and response latency are paramount.
• Specialized compute: tasks like rendering, scientific simulation, and large-scale data processing.

Selecting the compute

• Ownership versus rental: purchasing hardware involves significant upfront costs, along with power, cooling, and maintenance requirements. Leasing hardware involves periodic payments and scales according to workload demands.
• Dedicated versus shared: a dedicated GPU provides exclusive memory and speed for a single user. A shared, partitioned GPU serves multiple users at a lower cost, though with reduced individual performance.
• Model compatibility: the size of the model determines memory needs, which in turn dictates the required hardware class.

Capacity planning

• Fluctuating demand: training jobs tend to spike and dip. Procuring for peak capacity results in paying for idle time for much of the week.
• Queuing and scheduling: shared compute resources require a fair allocation method for cards, otherwise teams may impede one another's progress.

How NobleConsul can assist

Where applicable, NobleConsul may support the sizing of these workloads, the comparison of ownership and rental models, and the review of current capacity, as part of potential consulting activities.