The challenge
AI workloads can place significant demands on infrastructure, particularly during model training and development. As these workloads grow in complexity, organizations may need greater processing power, memory and storage, as well as consistent access to the resources supporting their applications.
Standardized hosting environments will not always provide the right resource profile for these requirements. An AI application may need a particular balance of compute, memory and storage, while development plans can also mean those requirements increase significantly over time.
For organizations running demanding or specialized AI workloads, dedicated servers provide an alternative where the underlying hardware can be configured around the application and its expected growth.
Our approach
When designing dedicated server infrastructure for an AI workload, our engineers start by understanding the application, its current resource requirements and how these are expected to develop.
A bespoke dedicated server can then be configured around these requirements, including the appropriate processing power, memory and storage. Because the physical resources are dedicated to a single customer, the workload has consistent access to the capacity provisioned for it without competing with other customers for the same underlying resources.
The hardware configuration can also reflect the specific characteristics of the workload. This might include different processor requirements, increased memory capacity or storage designed around the volume and type of data being processed.
Where AI projects are expected to become more demanding over time, scalability can be considered as part of the initial infrastructure design. Additional servers and resources can be introduced as requirements increase, allowing the hosting environment to develop alongside the application.
Our dedicated servers are fully managed, with technical engineers available 24/7/365 to manage and monitor the underlying infrastructure. This allows internal AI teams to focus on developing and operating their applications while the physical hosting environment is managed on their behalf.
The outcome
Dedicated server hosting gives AI teams access to physical infrastructure configured around the requirements of their workloads.
Dedicated resources provide consistent access to the compute, memory and storage provisioned for the application, while a bespoke configuration allows the hardware to reflect requirements that may not fit a standard hosting environment.
As an AI project develops, the infrastructure can also be expanded to accommodate increasing resource requirements, providing a foundation for development, training and production workloads.
When should you consider dedicated servers for AI workloads?
The right infrastructure for an AI workload depends on the model, its resource requirements and how it will be used. Dedicated servers may be appropriate where:
- Your AI workload has specific CPU, GPU or memory requirements that are not well matched to standard infrastructure configurations.
- You need consistent access to dedicated compute resources for model development, testing or training.
- Your application requires a bespoke balance of processing power, memory and storage.
- You expect infrastructure requirements to increase as the model develops or moves toward production.
- You have specific requirements around the location or jurisdiction in which your infrastructure is hosted.
- Your AI team wants to focus on model development rather than managing the underlying physical servers.
Customer case study
Symbolic Mind is developing neuro-symbolic AI architecture and needed dedicated infrastructure to train its EVA large language model (LLM). For the development and testing stage, the team required a powerful CPU server with significant memory capacity, as well as a solution that could scale as the project progressed.
After several providers were unable to meet these requirements within the required complexity and cost parameters, Symbolic Mind approached Hyve. Our engineers designed a bespoke 56-core CPU server around the requirements of the workload, with the infrastructure fully managed by our technical team.
Using the server to train EVA, Symbolic Mind achieved training speeds of 3 GB per hour, with plans to introduce additional high-performance infrastructure as its requirements increase.
Vadim Asadov, VP at Symbolic Mind, explained:
“We had a deep understanding of what we needed, and Hyve was able to translate this and build a solution that works for us.”
