What Is an AI Server?

Servers, simply put, are computers that provide a specific service to users or businesses, such as access to a database or application via the internet or other connectivity methods. The term AI servers refers to servers specifically built to handle the demands of AI workloads.

From a components perspective, AI servers incorporate a variety of discrete hardware elements, including:

• AI processors, including general-purpose CPUs
• AI accelerators, such as FPGAs, GPUs, and purpose-built AI hardware
• Networking technologies, such as Ethernet and Wi-Fi
• I/O, such as USB and Thunderbolt™ ports to support needed peripherals
• Server casing, which can take several forms to support application needs, such as fanless designs

To support the vast spectrum of workloads across the AI pipeline, AI servers are constructed to meet a variety of requirements. These can include considerations such as:

• Processing power: AI hardware selection will depend on the degree of compute capability the target workload requires. Some AI servers can fulfill their role using a stand-alone CPU architecture, especially when they take advantage of integrated AI acceleration capabilities. More demanding workloads may require the introduction of a discrete piece of accelerator hardware, such as a GPU or FPGA.
• Networking and I/O: Networking and I/O capabilities provide the vital connections critical to AI server use cases. Needed capabilities will depend on the kind of connectivity the AI workload requires, whether or not high-performance networking is needed, and the kind and number of peripheral devices, such as IoT sensors, that need to be supported, if any.
• Footprint and environment: AI servers can be deployed in various places, and their location will inform their form factor and environmental requirements. For example, AI servers at the edge often have stringent requirements for a small, economical, physical footprint and must be able to withstand challenging physical conditions like heat, temperature, moisture, or dust.

For many organizations seeking to harness the power of AI, cost-effectiveness, and rightsizing are critical concerns. AI servers need to meet their workload requirements with the most efficient hardware configuration possible to maximize ROI, meet business requirements, and facilitate scalability.

It’s also important to note the difference between AI servers and AI PCs. AI PCs are end-user computing devices people use to work on their AI tasks or interface with AI applications. AI servers, on the other hand, are primarily connected to other digital devices, including AI PCs, to enable services and applications.

Role of AI Servers

AI servers are a vital part of the AI technology landscape, and their use is ubiquitous across industries, use cases, and technologies. They are responsible for supporting AI workloads from training to deployment, wherever needed.

At the edge, lean and efficient AI servers are deployed to perform AI operations such as computer vision closer to data sources. To maximize efficiency and deployment flexibility, edge AI servers are often designed without discrete AI acceleration hardware. Edge AI workloads are typically deployment workloads, which frequently have less-demanding compute needs than training or fine-tuning. FPGAs, a type of AI processor and accelerator, are a particularly strong fit for edge AI servers because they offer energy efficiency and easy reconfigurability alongside accelerated performance for key AI functions.

In both on-premises and cloud data centers, AI servers, including deep learning servers, support AI fine-tuning and training by providing advanced compute capabilities. They’re also used in these environments to support deployment workloads, including computer vision, chatbots, and generative AI (GenAI). AI servers, including those deployed in high performance computing (HPC) environments, frequently incorporate discrete hardware accelerators such as GPUs, FPGAs, and purpose-built accelerator products to support AI needs.

Benefits of AI Servers

AI servers built with hardware components matched to AI workload needs unlock a range of benefits for businesses, including:

• Optimized performance: All AI workloads have different compute requirements. Matching the AI server architecture to these needs is critical to enabling cost-effective results. That said, AI workloads introduce demanding system requirements to achieve the required latency and throughput. AI servers are purposefully constructed to meet these needs, and often use AI accelerator technologies to meet them.
• Scalability: Often, AI servers are required on a large scale to meet application requirements, particularly in distributed edge deployments or HPC contexts. By carefully constructing the AI server architecture and taking advantage of software tools, such as server management platforms, enhanced AI server scalability can be achieved for specific AI workloads.
• Optimized power consumption: AI servers are often deployed at a very large scale to support use cases like AI-enhanced industrial operations, smart city initiatives, or highly complex operations like financial prediction or scientific simulations. These large-scale deployments, combined with the need for significant processing of the AI pipeline, can lead some AI server deployments to consume significant, costly amounts of energy. Right-sizing AI server hardware components allows organizations to better control their energy expenditure. Integrated AI acceleration and optimizations can also help power some AI use cases without specialized hardware accelerators.

AI Server Solutions

Virtually every AI application—across industries and use cases—involves a server of some kind. Likewise, servers play a central role in every phase of the AI workflow. Therefore, AI server solutions will vary based on workload needs.

Procuring AI server solutions for an organization can take several forms, including purchasing servers directly from an OEM, working with a solution provider, taking a DIY approach to building, or accessing cloud AI servers through the numerous providers available today. The best approach for you will depend on your team’s skillsets, your budget, your timing requirements, and the overall complexity and scalability requirements of your use case.

Since AI deployments often involve large networks of servers, server management technologies also play a key role in establishing an effective AI server strategy. A wide range of server management tools and platforms can be deployed to unlock deep visibility, real-time monitoring, and automated operations.