GPU Servers & HPC Infrastructure Consulting Services

GPU and high-performance computing handle resource-intensive operations, including AI model training and inference, simulations, rendering, and heavy data analysis. These workloads operate distinctly differently from standard office computing tasks.

Scope of Coverage

• Training workloads: The process of developing new models requires sustained, high-throughput compute resources over periods of hours or days.
• Inference workloads: Daily model execution focuses on minimizing per-request costs and optimizing response times.
• Specialized compute: Covers specific tasks such as rendering, scientific simulations, and large-scale data processing.

Selecting Compute Resources

• Ownership versus rental: Owning hardware involves significant upfront costs along with ongoing expenses for power, cooling, and maintenance. Renting hardware offers a pay-as-you-go model that scales flexibly with workload demands.
• Dedicated versus shared: Dedicated GPUs provide exclusive access to memory and speed for a single user. Shared, partitioned GPUs serve multiple users simultaneously, offering lower costs at the expense of individual performance.
• Model compatibility: The size of the model dictates the required memory, which in turn determines the appropriate hardware class.

Capacity Planning

• Variable demand: Training jobs exhibit fluctuating intensity. Procuring hardware for peak demand results in paying for idle capacity for most of the week.
• Queue and scheduling: Shared compute environments require fair allocation strategies to prevent teams from blocking one another.

How NobleConsul Can Assist

Where applicable, NobleConsul can support workload sizing, comparison of ownership versus rental models, and review of current capacity as part of their consulting services.