GPU & HPC

GPU and high-performance computing cover compute-intensive work: training and running AI models, simulations, rendering and data-heavy analysis. These workloads behave very differently from ordinary office computing.

What it covers

• Training workloads: building new models, which need sustained, high-throughput compute for hours or days.
• Inference workloads: running a model day to day, where per-request cost and response time matter most.
• Specialized compute: tasks such as rendering, scientific simulation and large data processing.

Choosing the compute

• Own versus rent: owned hardware costs a lot up front and needs power, cooling and maintenance. Rented hardware is paid per period and scales with the workload.
• Dedicated versus shared: a dedicated GPU gives one user exclusive memory and speed. A shared, partitioned GPU serves several users at lower cost and lower individual performance.
• Fit for the model: model size sets the memory requirement, and the memory requirement sets the hardware class.

Capacity planning

• Bursty demand: training jobs spike and drop. Buying for the peak means paying for idle time most of the week.
• Queue and scheduling: shared compute needs a fair way to allocate cards, or teams block each other.

How NobleConsul can help

Where relevant, NobleConsul may support sizing these workloads, comparing ownership and rental models, and reviewing current capacity, as possible consulting activities.