GPU Servers and High-Performance Computing Infrastructure Consulting Services
GPU and high-performance computing cater to compute-intensive demands, such as training and deploying AI models, running simulations, rendering visuals, and processing data-heavy analysis. These workloads operate distinctly differently from standard office computing environments.
Scope of coverage
- Training workloads: developing new models, which require sustained, high-throughput computing power for extended periods ranging from hours to days.
- Inference workloads: executing models on a daily basis, where the cost per request and response times are critical.
- Specialised compute: tasks including rendering, scientific simulation, and large-scale data processing.
Selecting the right compute
- Ownership versus rental: purchasing hardware involves significant upfront costs alongside requirements for power, cooling, and maintenance. In contrast, rented hardware is billed periodically and scales according to workload needs.
- Dedicated versus shared: a dedicated GPU provides exclusive memory and speed to a single user. Conversely, a shared, partitioned GPU supports multiple users at a lower cost, albeit with reduced individual performance.
- Model compatibility: the size of the model dictates memory requirements, which in turn determines the appropriate hardware class.
Capacity planning
- Fluctuating demand: training jobs often experience peaks and troughs. Procuring hardware for peak demand can lead to paying for idle capacity for most of the week.
- Queuing and scheduling: shared computing resources require fair allocation methods to prevent teams from obstructing one another's work.
How NobleConsul can assist
Where applicable, NobleConsul can support sizing these workloads, comparing ownership and rental models, and reviewing current capacity, serving as potential consulting activities.
GPU Servers & HPC Infrastructure Consulting Frequently Asked Questions
When should you consider GPU and HPC consulting?
Consider consulting before making a significant GPU purchase or entering a long-term rental contract, when cloud GPU costs are increasing, or when teams are experiencing delays due to shared compute limitations.
Is it cheaper to buy or rent GPUs?
Purchasing is typically more cost-effective if the GPUs are utilised consistently over several years. Renting is better suited for short-term projects and variable demand. It is essential to include power, cooling, space, and staff costs in the comparison. Many organisations choose to own hardware for steady workloads and rent for peak demands.
Can NobleConsul review current GPU usage before buying more?
Yes. The review demonstrates how busy your GPUs are and how teams share resources. It clarifies whether additional hardware is necessary or if improved scheduling or renting for peaks is sufficient.
How long does it take to set up on-premise GPU servers?
It usually takes a few weeks to several months. High-end GPUs may have long lead times, and racks might require additional power and cooling setup, so it is advisable to prepare the site while waiting for orders.
Is the recommendation tied to particular hardware vendors?
No. NobleConsul does not favour any specific GPU brand or supplier. For each option, whether bought or rented, the proposal outlines the setup cost, the total cost of ownership over the hardware's lifespan, and the training required for your team, enabling you to compare them side by side.