Sep 4, 2024
The rise of artificial intelligence (AI) isn’t just reshaping the tech landscape; it's fundamentally driving a new era of data center growth. The growth rate in the training compute of notable machine learning (ML) models has increased by 4.1x each year since 2010,1 fueling exponential demands on data centers to support high-intensity computational tasks. In this blog, we’ll explore the critical role of data center development in supporting the growing AI industry and how your business can leverage these developments to gain a competitive advantage.
Understanding the AI-Driven Demand for Data Centers
AI technologies require substantial computational power and data processing capabilities. From machine learning models that predict customer behavior to algorithms that process vast amounts of IoT data, the backend infrastructure needs to be as robust as the technologies it supports.
Specific AI applications driving this demand include large language models like GPT-3 that require massive computational resources for training, computer vision and image recognition models processing huge datasets, and scientific research areas like protein folding, genomics, and climate modeling.
Recent growth projections demonstrate the escalating need – the market for AI technologies reached $200 billion in 2023 and is expected to grow to over $1.8 trillion by 2030.2 And with this growing demand comes the drive to build AI at a larger scale, resulting in many companies spending over 80% of their total capital on compute resources to keep up.3
How Can Data Center Development Meet AI Requirements?
Data center development is becoming more sophisticated in response to the needs of AI applications. More organizations are opting to design or upgrade facilities to handle the extensive processing power required by AI, incorporating innovations in cooling technologies, power efficiency, and high-density configurations.
Some features of AI-ready data centers include:
- High Power Ratings: AI workloads, particularly those involving ML and deep learning, require significantly more power than traditional computing tasks. Data centers are adapting to accommodate these demands with higher power ratings, such as increased power density and better power usage effectiveness (PUE).
- Advanced Cooling Systems: High-performance computers generate significant heat, necessitating advanced cooling solutions to prevent overheating. Liquid cooling systems, which use coolants like water or refrigerants to remove heat, are becoming more prevalent in AI-focused data centers due to their efficiency in managing high-density compute loads.
- Seamless Scalability: The ability to scale rapidly is essential as AI development companies grow and their data needs evolve. Key scalability features include modular designs that allow for adding capacity and infrastructure that enables flexible provisioning.
- Flexible Deployment Models: Companies require flexibility in choosing the optimal deployment models to support their diverse AI workloads, including options like hybrid cloud solutions and support for different physical configurations such as cages, cabinets, or suites based on the demands of specific AI use cases.
- Operational Expertise: Data centers need professionals skilled in areas like AI workload optimization, GPU provisioning, and offering services beyond just the physical facility, such as remote hands support and managed services.
- High-Speed Connectivity: AI applications frequently require real-time data transfer and access to cloud resources, so high-bandwidth, low-latency network connectivity is crucial. Modern data centers can accommodate this need by prioritizing redundant fiber links and direct cloud on-ramps.
The symbiotic relationship between AI advancements and data center development is creating a cycle of growth that benefits both sectors. As AI technologies advance, data centers are being pushed to expand with sophisticated infrastructure capable of handling the intensive compute demands.
Planning for Data Center Expansion
For companies looking into data center expansion or establishment, considering the long-term requirements of AI operations is vital. This involves not just enhancing current capabilities but also future-proofing infrastructure against the evolving demands of AI technologies. Data center operators must ensure they can continue to provide the reliability, speed, and security that AI systems require.
If your organization is planning to expand your data center for AI, consider the following:
Location
Proximity to company operations and major connectivity hubs is important for reducing latency and enabling efficient data transfer. Many AI development companies choose data center locations close to major metropolitan areas with strong fiber networks.
Compliance and Security
Meeting industry standards and protecting data integrity is paramount, especially when dealing with sensitive data used in AI training. Data centers must have robust physical security, cyber security measures, and certifications like SSAE 18, PCI-DSS, HIPAA, ISO 27001, and others.
Sustainability
Incorporating green technologies and practices to minimize environmental impact is an increasing priority, with 76% of business leaders saying Environmental, Social, and Governance (ESG) is central to their business strategy.4 Using renewable energy sources, reclaimed water for cooling, and efficient data center design can reduce the carbon footprint.
Why Work With a Colocation Provider for Data Center Growth?
Rather than building their own data centers, many AI development companies are turning to third-party colocation providers that offer the specialized infrastructure and expertise needed to handle AI workloads. Colocation allows businesses to deploy their IT in a data center facility while the provider manages the underlying power, cooling, security, and connectivity.
Outsourcing data center operations enables AI development companies to focus on their core competencies while accessing state-of-the-art facilities designed for compute-intensive workloads. As AI models grow more complex and data sets expand, having a colocation partner with the ability to scale rapidly can provide a significant advantage.
The ability to expand on-demand via colocation can help AI businesses be more agile and cost-efficient compared to building out their own data centers. With shifting technology needs and the breakneck pace of innovation in AI, this flexibility is invaluable.
Partner With eStruxture for AI-Ready Colocation Services
For enterprise AI developers, understanding the relationship between AI applications and data center development is fundamental to making informed decisions about colocation services. By partnering with a data center provider that aligns with your organization’s specific needs, you can ensure your AI operations are supported by state-of-the-art infrastructure capable of handling complex AI tasks. For a deeper dive into choosing the right data center for your AI workloads, check out our comprehensive guide here.
At eStruxture, we recognize the importance of supporting the AI industry's infrastructure needs. Our colocation services are designed to provide the scalability, power, and connectivity that building high-demand AI applications requires. Contact eStruxture today to learn more about how our data center solutions can facilitate your company’s AI initiatives – and provide the room your business needs to grow.
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