GPU Servers & HPC Infrastructure Consulting Services
GPU and high-performance computing environments are designed for compute-intensive tasks, such as training and executing AI models, running simulations, rendering, and processing data-heavy analyses. 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, often lasting hours or days.
- Inference workloads: executing models on a day-to-day basis, where the focus is primarily on minimizing per-request costs and ensuring rapid response times.
- Specialized compute: handling specific tasks such as rendering, scientific simulations, and large-scale data processing.
Selecting the Right Compute Resources
- Ownership versus rental: Purchasing hardware involves significant upfront costs and requires investments in power, cooling, and maintenance. In contrast, renting hardware allows for periodic payments and the ability to scale resources according to workload demands.
- Dedicated versus shared resources: A dedicated GPU provides a single user with exclusive access to memory and processing speed. A shared, partitioned GPU accommodates multiple users at a lower cost, albeit with reduced individual performance.
- Model alignment: The size of the model dictates the memory requirements, which in turn determines the appropriate class of hardware.
Capacity Planning
- Variable demand: Training jobs often experience peaks and troughs. Purchasing hardware to handle peak loads means incurring costs for idle time for the majority of the week.
- Queuing and scheduling: Shared computing resources require a fair allocation mechanism for GPUs to prevent teams from hindering one another’s progress.
How NobleConsul Can Assist
Where applicable, NobleConsul can assist in sizing these workloads, comparing ownership against rental models, and reviewing current capacity as part of our consulting services.
GPU Servers & HPC Infrastructure Consulting Frequently Asked Questions
When should you consider GPU and HPC consulting?
It is advisable to seek consulting before making a significant GPU purchase or committing to a long-term rental contract, especially when cloud GPU costs are escalating or teams are experiencing delays due to shared compute resources.
Is it cheaper to buy or rent GPUs?
Purchasing is typically more cost-effective if the GPUs are utilized heavily over a period of several years. Renting is better suited for short-term projects or fluctuating demands. When making a comparison, include costs for power, cooling, space, and staff. Many organisations opt to own hardware for steady workloads and rent for peak periods.
Can NobleConsul review current GPU usage before buying more?
Yes. A review will demonstrate how busy your GPUs are and how teams are sharing them. This will help determine whether additional hardware is necessary, or if improved scheduling or renting for peak times is sufficient.
How long does it take to set up on-premise GPU servers?
The process usually takes from a few weeks to several months. High-end GPUs can have long delivery times, and racks may require upgrades to power and cooling first, so it is best to prepare the site while orders are being placed.
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 will outline the setup cost, the total cost over the hardware's lifespan, and the training your team requires, allowing for a clear side-by-side comparison.