What is an AI server? Why artificial intelligence needs specialized systems

AI servers are advanced computing systems designed to handle complex, resource-intensive AI workloads. Their capabilities go far beyond those of traditional servers: They are built to support workloads from training to deployment, and can manage massive (and continually growing) datasets, process complex AI computations and algorithms, run complicated simulations and support real-time insights.

Traditional servers consisting of CPUs, RAM, high-speed networking, hard disk drives (HDDs) and solid state drivers (SSDs), while critical to today’s high-performance computing (HPC), simply weren’t built to support such intense AI capabilities.

To balance speed, performance and scalability, AI servers incorporate specialized hardware, performing parallel compute across multiple GPUs or using other purpose-built AI hardware such as tensor processing units (TPUs), field programmable gate array (FPGA) circuits and application-specific integrated circuit (ASIC). They also use non-volatile memory express (NVMe) storage and high-bandwidth memory (HBM).

The role of AI servers

AI servers support all types of real-world use cases across finance, customer service, cybersecurity, manufacturing, healthcare and other industries. They fuel a variety of AI applications, including the following:

– Large language models (LLMs): These are the backbone of nearly every AI application today, particularly advanced generative AI systems that create text, code, images, video and 3D outputs.

[ Related:  What is AI networking? How it automates infrastructure ]

– Machine learning: An important branch of AI, ML is self-learning and uses algorithms to analyze data, identify patterns and make autonomous decisions. For instance, ML can be used for predictive maintenance, recommender systems, security scans and fraud and anomaly detection. They can also support customer service or employee chatbots.

–  AI training and inference: Training is when models are “taught” by ingesting various datasets; inference is their ability to respond to prompts and make predictions. Both of these are critical to ensure models are accurate and reliable.

– Natural language processing (NLP) and speech recognition: These understand and process text and audio input to support applications such as chatbots. NLP can be useful for basic customer service tasks and initial information-gathering, as well as for product recommendation and sentiment analysis.

[ Related: Networking terms and definitions ]

– Deep learning: DL uses neural networks to learn from data the way humans do. DL supports numerous NLP applications, and also helps with image recognition, coding and computer vision tasks.

– Edge AI: Some applications need to run as close to real-time data creation as possible. Edge AI is particularly critical in internet of things (IoT) environments — manufacturing facilities, self-driving cars, smart buildings, wearables — where accuracy and speed are paramount.

– AI agents: Agentic AI holds the promise of redefining workflows across the enterprise. Working autonomously, they are able to process data, move across workflows and take action on humans’ behalf. Agents are already augmenting workflows in early use cases including customer service (resolving tier 1 and some tier 2 issues) and sales (generating leads).

Choosing the right AI server

So what should you consider when selecting an AI server? It ultimately comes down to the types of workloads your teams will be working with. Here are some factors to keep in mind when looking at AI server options:

– Identify specific tasks you want AI to do: Applications that require minimal compute — lower-level NLP chatbots or simple gen AI — can run fine on standalone central processing units (CPUs) or simpler GPU architectures. But if you’re looking to deploy larger-scale systems (such as AI agents), you’re going to need architecture that is much more robust.

IDG

Work with vendors to understand the compute and memory requirements of your intended AI applications. This will help you pinpoint the right mix of hardware and software for your needs. Also, consider your networking and input/output (I/O) capabilities; these must be capable of supporting intended AI workloads. Even if you have advanced infrastructure in place, it’s not beneficial if your network can’t support it.

– Identify the deployment option that works for you. There are a variety of hosting options for AI servers: On-premises, in the cloud or a hybrid scenario.

On-premises servers (either lease or own) is a good bet for enterprises in compliance-heavy industries, but remember — upfront cost can be high and ongoing maintenance is a must. It’s important to consider space needs, cooling requirements and power consumption.

Cloud-based AI servers can be flexible and scalable, and provide the added bonus of vendor support. You can avoid high upfront costs, and pay-as-you-go pricing means you can fluctuate use up and down based on need.

Hybrid models can offer a happy medium, with computing running on both the cloud and on-premises. If you have the resources, this can allow you to build on the strengths of each model without having to choose one over the other.

Always remember: Design AI infrastructure for scalability, so you can add more capability when you need it.

Comparison of different AI server models and configurations

All the major players — Nvidia, Supermicro, Google, Asus, Dell, Intel, HPE — as well as smaller entrants are offering purpose-built AI hardware. Here’s a look at tools powering AI servers:

–  Graphics processing units (GPUs): These specialized electronic circuits were initially designed to support real-time graphics for gaming. But their capabilities have translated well to AI, and their strengths are in their high processing power, scalability, security, quick execution and graphics rendering.

– Data processing units (DPUs): These systems on a chip (SoC) combine a CPU with a high-performance network interface and acceleration engines that can parse, process and transfer data at the speed of the rest of the network to improve AI performance.

– Application-specific integrated circuits (ASICs): These integrated circuits (ICs) are custom-designed for particular tasks. They are offered as gate arrays (semi-custom to minimize upfront design work and cost) and full-custom (for more flexibility and to process greater workflows).

– Tensor processing units (TPUs): Designed by Google, these cloud-based ASICs are suitable for a broad range of aI workloads, from training to fine-tuning to inference.

– Field programmable gate array (FPGA) circuits: These are typically sold off-the-shelf and can be programmed after manufacturing to meet a variety of use cases. They are valued for their high performance, speed and flexibility.

Tips for optimizing AI server performance and scalability

Once adopted, it is critical that AI serves be continually optimized and managed. Much like AI itself, servers aren’t just set-and-forget. Keep in mind the importance of the following

– AI-powered monitoring and management: AI can support AI by autonomously tracking performance, automating certain tasks, offering up predictive insights, flagging for anomalies and supporting incident response.

– Load balancing devices: These distribute workloads across multiple servers to ensure that no one single server is overloaded. This is important to ensure high performance and to avoid bottlenecks and outages.

AI servers: Looking ahead

AI servers are playing an increasingly pivotal role as enterprises across industries race to implement sophisticated gen AI tools and AI agents. As they increase speed, performance, scalability and flexibility, they could hold the promise of unlocking the true value of AI.