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How the Rise of Generative AI is Impacting Data Centers and Network Infrastructures


Whether we’re ready for it or not, Artificial Intelligence (AI) is rapidly changing our lives. AI is already a multi-billion-dollar industry and is used in tech, education, art, healthcare and more.

Although AI has been around in the form of machine learning and data analytics applications used behind the scenes by many enterprises for years, the recent rise of Generative AI applications that respond to natural language inquiries from users is taking AI into exciting new arenas.

As these new applications are moving AI into the mainstream, the need for real-time, low-latency processing of Large Language Model (LLM) databases to provide on-the-fly responsiveness is having major impacts on data centers and networks. This means that existing data and communications infrastructures must undergo radical transformation and expansion to meet the new AI demands.

This article provides a forward look into the impact of new demanding generative AI applications on data centers, with a focus on the changes that will be necessary to support new requirements for scaling up massive computing and delivering low-latency responsiveness.
Figure 1 – With the rise of AI, what are the impacts on data centers?

Trends in New AI Use Cases

While generative AI and related applications are rapidly evolving, several emerging areas already show significant potential in the near term. Some leading new AI applications to watch for include:

Generative AI: Generative AI involves the creation of new data, such as images, text, music, or video, by AI models. This technology has applications in various fields, including content creation, design, gaming, and virtual reality. Generative adversarial networks (GANs), that are designed for iterative self-correction learning, have shown remarkable progress in generating realistic, high-quality content.

AI in Natural Language Processing (NLP): NLP continues to evolve, with advancements in language understanding, sentiment analysis, and language generation. OpenAI's GPT models, for instance, have demonstrated impressive language generation capabilities. Future applications include more natural and conversational virtual assistants, improved language translation, and enhanced content creation.

Edge AI: Edge computing combined with AI is gaining traction. By deploying AI algorithms and models directly on edge devices, such as smartphones, IoT devices, and autonomous vehicles, real-time decision-making and local data processing can be achieved. This enables faster response times, reduced latency, and improved privacy.

Explainable AI (XAI): Explainable AI focuses on making AI models and their decisions transparent and interpretable to humans. It aims to address the "black box" nature of deep learning models and provides insights into why certain decisions or predictions are made. XAI is crucial for building trust in AI systems, especially in domains like healthcare, finance, and law.

AI in Robotics and Automation (XAI): The integration of AI with robotics is advancing automation capabilities across industries. Collaborative robots, or cobots, equipped with AI can work alongside humans in manufacturing and assembly tasks. AI also enables robots to learn and adapt to new environments, enhancing their autonomy and versatility.

AI for Cybersecurity: As cyber threats become more sophisticated, AI is being employed to strengthen cybersecurity measures. AI algorithms can detect anomalies in network traffic, identify patterns of malicious activity, and prevent cyber-attacks. AI-driven cybersecurity systems can respond and adapt to evolving threats in real-time, providing enhanced protection.

AI in Personalized Medicine: AI is revolutionizing healthcare by enabling personalized medicine approaches. Machine learning models can analyze large-scale patient data, genetic information, and medical records to identify patterns and correlations. This can aid in disease diagnosis, treatment selection, and predicting patient outcomes, leading to more effective healthcare interventions.

AI for Climate Change and Sustainability: AI is being explored to address pressing environmental challenges. It can help analyze climate data, optimize energy consumption, predict weather patterns, and develop sustainable solutions. AI-powered systems have the potential to optimize resource utilization, reduce emissions, and contribute to environmental conservation.

These are just a few examples of the leading new AI applications that hold promise in the near term. As AI technologies advance and new research emerges, we can expect further breakthroughs and applications across a wide range of industries.

AI Impacts on Data Centers and Networks

The rise of artificial intelligence (AI) will have a significant impact on networks and computing data centers. Here are some important ways in which AI will influence these key infrastructure elements:
Figure 2 – Bandwidth speed and performance demands are only going to increase with advanced developments in AI.
Increased computational demands: AI applications, particularly deep learning algorithms, require substantial computational power to process and analyze vast amounts of data. This demand for computational resources will drive the need for more powerful and efficient hardware in data centers. Processors and accelerators designed specifically for AI workloads, such as graphics processing units (GPUs) and tensor processing units (TPUs), are already being deployed to meet these requirements.
Network bandwidth requirements: AI systems often rely on large datasets for training and continuous learning. As AI models become more complex and datasets grow, the demand for high-speed data transfer within data centers and across networks will increase. This will necessitate improvements in network infrastructure, such as higher-capacity switches, routers, fiber optic cables, and internal system interconnects to ensure efficient data movement.
Edge computing and distributed AI: The growth of AI is driving the need for real-time and low-latency processing. Edge computing, where AI computations occur closer to the data source or end-users, is becoming increasingly important. By decentralizing AI processing to edge devices, such as IoT devices or local servers, organizations can reduce latency and bandwidth requirements. This shift towards edge computing will require the development of distributed AI architectures and the integration of AI capabilities into edge devices and networks.
Figure 3 – AI may elevate privacy and data security risks.
Enhanced security measures: As AI becomes more prevalent, both in data centers and at the edge, the need for robust security measures will increase. AI can be employed to detect and respond to security threats in real-time by analyzing network traffic for anomalous behavior and identifying potential cyberattacks. Conversely, adversaries may also exploit AI techniques to launch more sophisticated attacks. Therefore, data centers and network infrastructure must incorporate AI-based security mechanisms to counter potential threats effectively.
Figure 4 – Higher energy demands as compared to traditional types of computing.
Increasing Power Demands: Hyperscale data centers, with thousands of servers managing petabytes of data in 100,000s of square feet of space, provide efficiency for quickly processing voluminous amounts of data but have massive power requirements. According to the International Energy Agency (IEA), data center energy use was in the 220-to-330 Terawatt-hours (TWh) range in 2021, representing roughly 0.9% to 1.3% of total global electricity demand. This is more energy than some countries consume each year and will grow significantly to support AI.
Network optimization and automation: In addition to the above impacts, AI also can be utilized to optimize network performance and efficiency. Machine learning algorithms can analyze network traffic patterns, predict potential bottlenecks, and dynamically allocate resources to ensure optimal performance. AI-driven network automation can also enable self-healing networks, where AI systems detect and resolve network issues automatically, reducing the need for manual intervention and improving overall network reliability.

Overall, the rise of AI will drive the demand for more powerful hardware, increased network bandwidth, the development of edge computing capabilities, and higher power usage. It will also make possible AI-enabled improvements in network optimization, automation, and enhanced security measures to support the growing demands of AI applications.

Tech Innovations to Support Infrastructure Improvements for AI

Addressing these AI-driven demands will require advances in key enabling technologies, including:
  • Interconnects (higher density, robust, automation-friendly)
  • Connectors (solder-free, customized, modular)
  • Enclosures (power sources, cooling and thermal solutions, rackmount)
  • Module-level and vertical integration
Over decades of serving the Information and Communications Technology (ICT) sector, Interplex has earned our track record of innovation and trusted leadership with both enabling technological building blocks, such as custom connectors, and delivering vertical integration solutions, such as 41U OCP rack and chassis.

Key enabling technologies include Press-Fit interconnects, customizable busbars, SSD carriers, HDD carriers, heatsinks, network enclosures and custom design application-specific vertical integration solutions.
Figure 5 – Our competency in stamping, molding and die casting allows us to optimize designs for manufacturability that enables customers to enjoy cost reduction of about 22%.
Our innovative designs, production expertise and deep knowledge in customizing next-generation cloud computing and enterprise storage solutions is enabling faster and more reliable networking and communications infrastructure to support the shift towards 5G and Internet of Things (IoT). Currently, Interplex design teams are also well on their way to creating new innovations to address the AI demands on ICT infrastructures.


Figure 6 – Interplex has 33 manufacturing facilities across the globe.
As the global adoption of AI applications accelerates and new generative AI offerings become increasingly mainstream, the impacts will be felt throughout the communications, data management, and cloud computing infrastructures. Specialized AI-focused processors and massive data storage capabilities will be the most obvious changes but virtually all the supporting and enabling technologies will also be critical for success.

At Interplex, we are proud and excited to play a key role in helping to innovate designs and deliver targeted solutions all up and down the technology stack that will fuel the sweeping changes needed to make the promise of new AI capabilities a reality for companies and consumers everywhere in the world.

With our manufacturing facilities in diverse locations, customers can tap into our regional value chains to reduce time-to-market and transportation costs and improve response time to changes.

Discover how we can customize solutions that meet the challenging needs with the rise of AI.

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