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Artificial intelligence (AI) continues to gain traction across many industries, helping us find new and innovative work methods. With these ongoing developments, one critical concern that can’t be ignored is AI’s impact on data centers.
As AI becomes more involved in data center operations, high energy use becomes a significant issue.
AI tasks, especially machine learning (ML) and deep learning (DL), require a lot of computing power, raising energy demands. This increase in energy drives up costs and will continue to impact organizations as space and power become more limited.
To get some perspective on AI’s impact on data centers, we consulted with our partners at Flexential. From challenges with sustainability to optimizing AI performance, they shared what you need to know in this rapidly changing landscape.
The Next Decade of Data Growth
Over the coming decade, the expectation is that data growth will see exponential growth. Driving this growth are three key factors:
#1. More connected devices:
The Internet of Things (IoT) is already linking billions of devices, which is expected to reach trillions in the future. Each device will create more data, increasing the total data volume in the world.
#2. AI growth:
AI continues to become more advanced and sophisticated and is now being leveraged in various ways to create and collect data. From diagnosing diseases to monitoring traffic patterns, we’ll continue to see AI becoming more prevalent in everything we do.
#3. Increased cloud computing:
Thanks to cloud computing, storing and analyzing data has never been more accessible or affordable. As a result, we’re seeing increased adoption of cloud-based data storage and analytics solutions, leading to additional data growth.
What we can anticipate from AI’s impact on data centers: According to one report from 451 Research, over 50% of enterprises predict a moderate to significant increase in the amount of data used to build or train AI/ML models in the next 12 months.
We can also expect that in the next ten years, data will be generated from many different sources, including:
Social media: Social media platforms generate vast amounts of data that can be used to target ads more effectively, track trends, and understand consumer behavior.
E-commerce: These platforms collect data on how customers browse, what they buy, and their product reviews. This data can then be leveraged to enhance product recommendations, improve marketing campaigns, and prevent fraud.
IoT devices: Even more data will be created, including sensor information, location details, security data, and usage data.
Healthcare: Using medical images, patient records, and clinical trials, healthcare providers generate data that can be used to identify new treatments, improve diagnosis and treatment, and reduce healthcare costs.
Managing Data Volume, Security, and Network Latency
While the exponential data growth expected in the coming years will open the doors to more innovation and advancements, AI’s impact on data centers poses some specific challenges.
High-performance computing (HPC) densities are only growing. For example, the current NVIDIA Blackwell chips with the GB200 machines run 120 -130kW per cabinet. This is 10x the average cabinet power density from a few years ago.
The volume of data generated in the coming years is on pace to exceed significantly the current storage capacity, which means new storage technologies will need to be developed. This volume will also pressure existing data processing systems, creating demand for newer technologies.
We also need to consider the need for improved data security. As the data volume increases, so do the potential opportunities for hackers to target. Data security solutions must continue to advance and keep pace as cybercriminals become more sophisticated and look for more opportunities to access sensitive data.
When deploying tech in a data center, multiple factors must be considered to enhance and optimize AI accuracy, speed, and efficiency. Training and inference processes are different, access to high-quality data is crucial, and managing network delays is essential.
Training and inference are two critical steps in the AI process, each with different goals. Training means giving a model lots of labelled data to learn to make accurate predictions. Inference is when the trained model uses what it learned to make predictions on new data it hasn’t seen before. The primary differences between these two processes are resource allocation, time sensitivity and computation requirements.
Successful AI deployments depend on the accessibility and quality of the data sets. Data diversity and integrity are critical, as AI models require various sources to minimize biases and ensure generalizability. Privacy, security, data governance, and compliance must also be prioritized to ensure data is handled responsibly and data centers adhere to regulations.
Finally, network latency can significantly impact AI applications in data centers, so minimizing this is crucial, particularly for interactive or real-time applications such as natural language processing (NLP) systems. To effectively manage network latency, data centers must consider their network infrastructure and how edge computing can be leveraged when distributing AI workloads.
The AI Impact on Data Centers: Create a Strategy for Success
The reality of the current landscape and AI’s impact on data centers is that regardless of whether your organization has an AI strategy in place, it’s being affected by AI’s evolution. For example, as AI matures, power densities in the data center will continue to increase for these HPC footprints.
As the AI market continues to evolve, understanding its critical impacts on your company will help you make an informed decision about selecting the right data center provider.
Bridgepointe is the leader in the data center and cloud advisory market, having completed over 1,200 successful projects for our clients. To ensure you get the best solution for your next data center project, download this guide, which shares the ten critical considerations for selecting a data center.
