Shaping the Future: Key Trends Driving the Data Center Industry in 2025

1. Summary

• As of May 16, 2025, the data center industry is witnessing a transformative phase characterized by an exponential increase in data generation, driven largely by advancements in artificial intelligence (AI). This unprecedented growth has engendered a pressing need for improved infrastructure capabilities. Reports suggest that the volume of data generated by AI applications is not just increasing but evolving in complexity, thereby demanding more sophisticated computational resources for efficient processing. The implications of this surge extend beyond mere data management; they encompass strategic shifts in processing architectures and energy consumption, with forecasts indicating that the energy requirements for data centers could potentially double by 2030 due to AI-related workloads. This fundamental shift necessitates that operators adapt their facility designs to handle massive datasets effectively and ensure rapid processing capabilities, marking a pivotal point in data center operations.

• Profitability and reliability remain critical benchmarks for data center operations, with the 'Five Nines' uptime metric—denoting 99.999% uptime—exemplifying industry standards for service availability. As organizations increasingly integrate AI technologies, the demand for higher uptime becomes inevitable, compelling investment into higher redundancy, proactive maintenance measures, and innovative architectures. The ongoing evolution includes comprehensive strategies that adopt modular designs and advanced predictive maintenance capabilities to stay ahead of potential disruptions. Consequently, data center operators are not only prioritizing uptime but are also focusing on architectural innovations that leverage software-defined infrastructure to enhance real-time operational responsiveness.

• Additionally, sustainability concerns are surging within the sector as energy consumption escalates alongside rising global demands. As noted, regions are implementing regulatory measures, such as moratoriums on new data center developments, urging industry stakeholders to rethink energy practices to enhance overall environmental performance. As companies like Google and Microsoft set ambitious targets for carbon-neutral operations by 2030, innovative practices such as immersion cooling, renewable energy integration, and efficient power management systems are being adopted as standard operating procedures to reconcile business objectives with ecological responsibilities.

• The proliferation of edge computing and hybrid cloud architectures continues to reshape operational capabilities, particularly in light of IoT and the imminent deployment of 5G technology. These developments foster low-latency environments essential for applications requiring immediate data access. Organizations are increasingly strategizing towards multi-cloud solutions that harness the benefits of both edge and centralized resources, paving the way for enhanced operational agility while fulfilling regulatory compliance and security measures—particularly as cybersecurity threats evolve within this complex landscape.

2. AI-Driven Data Growth and Scalability Challenges

• 2-1. AI-driven surge in data generation

• As of May 16, 2025, the data center industry is experiencing an unprecedented surge in data generation primarily driven by advancements in artificial intelligence (AI). Reports indicate that AI applications are not only increasing the volume of datasets generated but also demanding more complex computational resources to process this data. For instance, generative AI models, such as those used for language processing and autonomous systems, require extensive data for training, leading to heaps of information that must be managed effectively within data center operations. According to insights from industry analyses, the energy consumption of data centers could double from 2025 to 2030, underscoring the growing needs attributed to AI-related workloads. This surge in data is reshaping how data centers approach infrastructure scaling, as the ability to handle massive datasets becomes paramount.

• 2-2. Impacts on storage and processing demands

• The rise of AI has significant implications for both storage and processing demands within data centers. With AI workloads being inherently data and compute-intensive, data centers have begun to upgrade their infrastructure to accommodate these needs. The increasing power density in data centers now averages between 4 kW to 6 kW per rack, with projections suggesting that it may rise further as AI becomes more central to operations. High-performance computing (HPC) clusters play a crucial role in supporting these workloads, utilizing specialized hardware such as GPUs and TPUs to accelerate data processing. Furthermore, data centers are investing in high-capacity storage solutions to handle the influx of data, employing hard disk drives (HDDs), solid-state drives (SSDs), and network-attached storage (NAS) systems to optimize speeds in data retrieval and processing. Collectively, these changes signify a vital shift in data center design and architecture as they are forced to become more flexible and scalable, ensuring they can not only store vast amounts of data but also process them in near real-time.

• 2-3. Scalability and architectural responses

• In response to the challenges posed by data generation and processing demands, data centers are adapting their scalability strategies and architectural designs. As derived from ongoing analyses, companies are increasingly moving toward modular designs that allow for dynamic growth and flexibility. This includes leveraging virtualization and containerization technologies that enable them to optimize resource allocation according to real-time workload requirements.

• Additionally, there is a burgeoning interest in hybrid cloud models which combine on-premises data centers with public cloud resources, thereby allowing enterprises to scale quickly without significant capital expenditure. Recent reports have highlighted the challenges posed by energy demands as data center operations sometimes clash with local infrastructure capabilities, leading to stricter regulations from authorities in places like Dublin and Amsterdam concerning new data center developments. The sustainability pressures and the need for efficient energy consumption practices lead to an imperative for data centers to rethink their architectural responses, creating facilities that are not only capable of handling current AI demands but are also designed with future scalability in mind.

3. Infrastructure Reliability and the Five-Nines Imperative

• 3-1. Importance of 99.999% uptime

• The concept of 'Five Nines' or 99.999% uptime is an industry benchmark for data center reliability, denoting a maximum allowable downtime of approximately 5.26 minutes per year. This metric has become crucial as the demand for uninterrupted service continues to escalate, especially in sectors like finance, healthcare, and AI-driven applications, where even minimal downtime can result in significant financial loss, data corruption, or compromised user trust. As such, achieving this level of reliability necessitates substantial investments in backup systems, redundancy measures, and real-time monitoring capabilities to ensure operational continuity, thereby affirming that downtime can have far-reaching implications beyond mere metrics. Furthermore, as AI technologies become more integrated into business operations, the push for even tighter uptime requirements is evident, ensuring that critical processes operate smoothly without interruptions.

• 3-2. Architectural strategies for high availability

• To attain Five Nines availability, data centers are increasingly adopting various architectural strategies that emphasize redundancy, fault tolerance, and proactive maintenance. These strategies include implementing Geographic Redundancy—where data centers are dispersed across different regions to mitigate the risks of localized failures; using Modular Data Center Designs which allow for scalable expansion and rapid deployment; and incorporating advanced cooling systems that maintain optimal operating conditions. Additionally, the architecture embraces Software-Defined Infrastructure to enhance flexibility and automation, thereby optimizing resource allocation and reducing human error, which is a common cause of outages. Importantly, the design incorporates real-time monitoring and predictive maintenance capabilities, which allow operators to anticipate potential failures and resolve issues proactively before they lead to significant downtime.

• 3-3. Supporting mission-critical and AI workloads

• As the reliance on AI workloads intensifies, the infrastructure supporting these applications must cater to the unique requirements that such workloads demand, including high-bandwidth networking, substantial storage capacity, and extraordinary processing power. High-Performance Computing (HPC) clusters, which consist of interconnected servers capable of parallel processing, are becoming a norm in data centers housing mission-critical AI applications. Specialized hardware, such as Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs), play a pivotal role in ensuring these demanding workloads run efficiently. Furthermore, these infrastructures support advanced networking solutions that guarantee low-latency communication between components, addressing the intricate computational needs of AI models effectively. Furthermore, as evidenced in the published information, AI's rapid growth is directly influencing power consumption and infrastructure design, creating additional challenges and opportunities for data centers to innovate and ensure continuous support for mission-critical tasks.

4. Evolving Compute Architectures: CPU, GPU and Beyond

• 4-1. CPU vs GPU trade-offs for machine learning

• The debate between using Central Processing Units (CPUs) and Graphics Processing Units (GPUs) in machine learning workflows has become increasingly pertinent as AI applications continue to expand. CPUs, known as the 'brains' of computers, are general-purpose processors designed for various tasks, making them ideal for sequential operations such as data preprocessing and managing lighter workloads. They consist of a limited number of powerful cores optimized for fast processing but excel in handling tasks requiring precision and low latency. As such, when it comes to tasks that involve smaller datasets or require individual calculations, CPUs can often be the more cost-effective option.

• On the other hand, GPUs are specialized processors designed to excel in parallel processing, making them invaluable for machine learning tasks that involve extensive computations, such as training deep learning models. GPUs contain thousands of smaller cores that process data simultaneously, significantly accelerating tasks that require multiple calculations, such as matrix operations commonly seen in AI workflows. For example, during a training session for large models, GPUs can perform billions of operations per second by distributing the workload across their numerous cores. However, this immense power comes with challenges, especially in light of current supply chain constraints and the overall high demand for GPUs in various sectors, including AI, cryptocurrency, and gaming.

• As of May 2025, the GPU market is still recovering from significant shortages triggered by the COVID-19 pandemic, which has resulted in supply chain disruptions and heightened competition for these critical components. Consequently, businesses may find themselves in a position where accessing GPUs becomes a bottleneck for machine learning projects, leading organizations to consider leveraging CPUs for tasks such as lightweight model inference or smaller-scale analyses. Companies are increasingly looking for hybrid solutions that incorporate both CPUs and GPUs into their workflows, using each for the specific tasks they perform best.

• 4-2. GPU supply-chain dynamics and shortages

• The current landscape of GPU availability remains strained due to ongoing supply chain issues and an ever-increasing demand driven primarily by artificial intelligence applications. As organizations strive to implement AI solutions, the need for GPUs has surged dramatically, impacting their availability. Reports indicate that a single large language model, such as those developed by OpenAI, can require thousands of GPUs for training—demonstrating the enormity of resources needed to continue advancing in this field. For instance, the development of GPT-4 necessitated approximately 25, 000 NVIDIA A100 GPUs, reflecting how GPU shortages have serious implications for the pace of innovation within AI.

• As of May 16, 2025, the ramifications of this scarcity are becoming increasingly evident, leading many organizations to explore alternative strategies to optimize their AI capabilities. Consequently, the GPU market is witnessing a shift where businesses prioritize investments in accessibility and scalability to ensure they remain competitive in an AI-driven market. Many firms are now advocating for better management of existing GPU resources while also accelerating investments in alternative technologies, including general-purpose computing on CPUs or exploring next-generation accelerators that may help ease reliance on traditional GPUs.

• 4-3. Emerging accelerators and specialized hardware

• With GPUs experiencing shortages and escalating prices, attention has turned towards emerging accelerators and specialized hardware as viable alternatives for machine learning and AI workloads. Technologies like tensor processing units (TPUs), designed specifically for neural network processing, are gaining traction due to their optimized architecture, which allows them to perform certain computational tasks faster than conventional GPUs, particularly in deep learning applications.

• As organizations attempt to navigate the limitations of the current GPU market, other forms of specialized hardware such as FPGAs (Field Programmable Gate Arrays) and ASICs (Application-Specific Integrated Circuits) are also emerging as attractive solutions. These types of hardware can be tailored for specific machine learning tasks, providing flexibility and potentially lower power consumption. Moreover, businesses are increasingly exploring hybrid architectures that utilize a blend of CPUs, GPUs, and these newly emerging units to balance performance, cost, and accessibility in their workloads. This evolution in hardware reflects the industry’s adaptability in response to the rising complexities and demands of AI applications.

5. Sustainability and Energy Efficiency

• 5-1. Rising energy consumption trends

• As of May 16, 2025, the data center industry is experiencing unprecedented energy demand, primarily driven by the surge in AI technologies and the expansion of cloud computing. Forecasts indicate that global electricity usage by data centers could nearly double from 536 terawatt-hours (TWh) in 2025 to approximately 1, 065 TWh by 2030. This alarming increase raises concerns regarding grid reliability, as evident from reports suggesting power quality issues in regions heavily impacted by data centers. For example, critical analyses have pointed out how the energy-intensive nature of AI data centers can potentially strain existing electrical grids, affecting both power quality and the reliability for nearby communities.

• Recent regulatory efforts have responded to these rising demands. In Ireland, for instance, policymakers have imposed a ban on new data center developments around Dublin until 2028, aiming to manage energy consumption levels while ensuring grid stability. Similar situations are unfolding in other locales; Amsterdam maintains a moratorium that restricts additional data center operations, reflecting heightened awareness of the sector's environmental impact and energy appetite.

• Moreover, the environmental ramifications of these energy demands are significant. An analysis by S&P Global indicated that carbon emissions from data centers could nearly double by 2030, as many facilities continue to rely on fossil-fuel sources, including natural gas, for their power supply. As the industry struggles to reconcile the need for robust energy resources with sustainability initiatives, stakeholders face growing scrutiny from regulators and environmental advocates alike.

• 5-2. Innovations in cooling and power management

• To address the surging energy demands, data centers are innovating in cooling and power management technologies. These advancements are critical for improving sustainability outcomes while ensuring operational efficiency. One prominent trend is the adoption of immersion cooling technologies, which have the potential to substantially reduce the energy consumption associated with conventional cooling methods. Immersion cooling involves submerging servers in a thermally conductive liquid, drastically improving heat dissipation and reducing the reliance on traditional air conditioning systems.

• Moreover, the industry is beginning to explore renewable energy integration and on-site generation solutions, particularly through the use of solar and wind power. Many operators are investigating colocated solar farms or procuring energy through power purchase agreements (PPAs) with green energy providers. These strategies not only help mitigate the carbon footprint but also increase the resilience and reliability of power supply.

• Other notable innovations include advancements in power management systems that optimize energy usage through smart technology and analytics. By employing AI-driven predictive algorithms, facilities can effectively balance load demands, improving overall energy efficiency. This technology can facilitate better resource allocation, ensuring that power is utilized judiciously, which is increasingly necessary given the impending regulatory pressures and societal expectations for greener operational practices.

• 5-3. Industry targets for carbon footprint reduction

• In light of the escalating environmental scrutiny, many leading tech companies have set ambitious targets for carbon footprint reduction. Firms such as Google, Microsoft, and AWS aim to operate entirely on carbon-free energy by 2030. However, achieving these sustainability goals requires navigating a complex landscape characterized by rapid growth in energy demands, notably due to AI applications.

• The data center industry is also seeing a shift towards adopting sustainable design principles. New facilities are increasingly being constructed with resource-efficient building techniques, employing sustainable materials, and integrating renewable energy sources. Furthermore, organizations are focusing on achieving energy efficiency certifications, such as LEED or BREEAM, which not only help meet compliance but also enhance corporate sustainability performance in the eyes of investors and customers.

• Despite these positive developments, challenges remain. A significant portion of new data center energy demand is projected to be met by natural gas, which, while cleaner than coal and oil, complicates long-term decarbonization efforts. Major players in the sector must reconcile the immediate need for reliable energy sources with longer-term sustainability commitments, ensuring that their growth trajectories align with broader environmental goals. Coupled with increasing regulatory frameworks that aim to limit carbon emissions, the industry must innovate rapidly to balance the dual pressures of operational efficiency and sustainability.

6. Edge and Hybrid Cloud Expansion

• 6-1. Proliferation of Edge Data Centers

• As of May 16, 2025, the proliferation of edge data centers continues to reshape the data center landscape significantly. The push towards decentralized computing has bolstered the establishment of numerous edge facilities designed to process data closer to the source of generation. This change, driven by the increasing demand for low-latency applications, particularly those associated with IoT and real-time analytics, has allowed enterprises to enhance their operational efficiencies while reducing the necessity for lengthy data transfers to centralized data centers. Notably, governments and urban developers are increasingly recognizing the role of edge data centers in supporting local economies and technological ecosystems, which has led to both public and private investments in this area.

• 6-2. Latency-Sensitive 5G and IoT Applications

• The integration of 5G technology has been pivotal in enabling latency-sensitive applications, particularly within the IoT sphere. As of 2025, the deployment of 5G networks is accelerating, providing the necessary infrastructure for real-time data processing and immediate responses in applications ranging from autonomous vehicles to smart city management systems. This ongoing synergy between edge computing and 5G networks is transforming business models across various sectors by facilitating instantaneous data access, which is essential for decision-making processes in dynamic environments. The intersection of these technologies is not merely enhancing user experiences but also driving new revenue streams for service providers and enterprises alike, marking the significance of edge computing in a rapidly evolving technological framework.

• 6-3. Integration Strategies for Edge and Central Clouds

• As the data landscape becomes increasingly complex, the integration of edge and central cloud infrastructures has become more critical. Organizations are now employing multi-cloud strategies that leverage the strengths of both edge and central cloud resources. As of May 2025, businesses and IT leaders are focusing on seamless data management and orchestration between these environments to ensure consistent performance and security. Effective integration strategies not only optimize the performance of applications but also enhance the scalability of resources in response to fluctuating demands. Leading cloud providers are actively developing solutions that facilitate interoperability, enabling businesses to harness the unique advantages of edge computing—such as reduced latency and localized data processing—while maintaining the vast storage and advanced compute capabilities of central cloud infrastructures.

7. Security, Compliance, and Zero-Trust Architectures

• 7-1. Evolving Cyber-Threat Landscape

• As of May 16, 2025, the cyber-threat landscape has significantly evolved, driven by a combination of increased digitization, the proliferation of IoT devices, and sophisticated attack methodologies. Organizations are facing a growing array of potential threats ranging from ransomware attacks to advanced persistent threats (APTs). The integration of AI in cyber warfare has made these threats even more daunting, as attackers leverage machine learning algorithms to refine their strategies and evade traditional security measures. Security incidents are no longer isolated occurrences; they can result in severe damage to organizational reputation, financial losses, and legal repercussions, necessitating an enhanced focus on robust security architectures, such as zero-trust models.

• 7-2. Regulatory and Compliance Drivers

• The regulatory landscape pertaining to data security and privacy has intensified remarkably, compelling organizations to bolster their compliance frameworks. Emerging regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) have set high standards for data protection, while sector-specific regulations, such as those applicable to financial services, healthcare, and telecommunications, impose stringent data safeguarding requirements. This regulatory tightening underscores the need for ongoing audits and compliance checks, while also promoting the adoption of technologies that enhance transparency and accountability. The consequences of non-compliance are severe, ranging from hefty fines to crippling reputational damage, prompting many organizations to engage in preemptive actions to ensure adherence to these evolving mandates.

• 7-3. Adoption of Zero-Trust Security Models

• The shift towards zero-trust security architectures is becoming a centerpiece in the strategic security outlook for organizations across various industries as of 2025. Underlying the zero-trust philosophy is the principle of 'never trust, always verify, ' which emphasizes continuous verification of user and device identities, regardless of their location relative to the network perimeter. This model stands in contrast to traditional security models that often rely heavily on perimeter defenses. The increasing complexity of acquiring and managing identities in hybrid and cloud environments makes zero-trust principles particularly relevant. Furthermore, organizations are integrating technologies such as micro-segmentation, identity and access management (IAM), and secure access service edge (SASE) to realize the full potential of zero-trust frameworks. Consequently, organizations are not only enhancing their resilience against external threats but also addressing insider risks that are often more challenging to detect.

8. Automation and Predictive Maintenance

• 8-1. AI-driven operational automation

• As of May 16, 2025, the integration of artificial intelligence (AI) in operational automation processes in data centers is transformative. AI technologies enable organizations to streamline operations, thereby enhancing efficiency and minimizing manual intervention. By leveraging machine learning algorithms, data centers can now analyze vast amounts of operational data in real time, allowing for dynamic adjustments that optimize resource allocation and task scheduling. This automation not only reduces the likelihood of human error but also enhances responsiveness to workload changes, which is crucial in the context of fluctuating demands driven by AI and big data applications.

• 8-2. Predictive maintenance to reduce downtime

• Predictive maintenance, a key component of modern data center management, employs AI and data analytics to forecast equipment failures before they occur. As of the present, many organizations are leveraging predictive analytics to monitor the health of critical infrastructure continually. Sensors collect performance data, which is then analyzed to identify patterns that may indicate potential failures. This proactive approach shifts maintenance from a reactive to a predictive model, significantly reducing unplanned downtimes and extending the lifespan of hardware. Reports indicate that companies implementing these strategies have successfully decreased equipment-related downtime by substantial percentages, thereby improving operational efficiency.

• 8-3. Cost and resource optimization

• The deployment of AI-driven automation and predictive maintenance strategies leads to significant cost savings and resource optimization in data centers. By reducing the frequency and severity of equipment failures, organizations can lower maintenance costs and increase ROI on their hardware investments. Additionally, operational automation facilitates better energy management practices, essential in a climate increasingly sensitive to energy usage and environmental impact. Data from industry projections suggest that optimized operations can decrease energy consumption by up to 20% while still meeting performance and reliability standards. As firms strive for sustainability and efficiency, the role of automation in cost management continues to grow in prominence.

Conclusion

• In conclusion, the data center industry in 2025 stands at a crucial junction where rapid technological advancements intersect with increasing demands for reliability, efficiency, and sustainability. The dual challenges of coping with substantial AI-driven data growth while adhering to stringent uptime requirements—particularly exemplified by the 'Five Nines' standard—underscore a critical need for operators to innovate continuously. Balancing these operational pressures alongside ambitious decarbonization targets highlights the importance of evolving hardware paradigms and agile infrastructures capable of accommodating future workloads.

• The shift toward GPU-accelerated processors and specialized computing hardware poses additional strategic challenges, particularly within supply chain management and architectural design. As organizations navigate this landscape, leveraging edge and hybrid cloud models will be vital in meeting the latency demands of modern applications while ensuring robust security postures through zero-trust architectures. Moreover, automated management supported by predictive maintenance strategies can enhance operational resilience, optimize costs, and significantly reduce downtime.

• As this industry continues to evolve, the interconnected trends of AI integration, sustainability initiatives, and the adoption of advanced infrastructure tools promise to shape the future of data center investments and operational practices through the remainder of 2025 and beyond. Stakeholders who act swiftly to adopt a holistic approach—incorporating software-defined infrastructure, AI-driven operational efficiencies, and commitments to green energy solutions—will position themselves favorably in a competitive marketplace, ultimately driving innovation and sustainability in a burgeoning digital economy.

Glossary

• Data Center: A facility used to house computer systems and associated components, such as telecommunications and storage systems. Data centers enable centralized management of digital assets, providing a scalable environment for data storage, processing, and services, particularly as data requirements grow due to advancements in AI and cloud computing.

• AI (Artificial Intelligence): A branch of computer science focused on the creation of systems capable of performing tasks that typically require human intelligence. As of 2025, advances in AI are driving significant growth in data generation, resulting in increased demands for processing capabilities within data centers.

• GPU (Graphics Processing Unit): A specialized electronic circuit designed to accelerate the processing of images and graphics. In data centers, GPUs are increasingly crucial for machine learning applications that require parallel processing and high computational power, particularly in training AI models.

• Five Nines (99.999% Uptime): An industry benchmark for data center reliability, indicating a maximum allowable downtime of about 5.26 minutes per year. Achieving this level of uptime is critical for businesses that depend on continuous and reliable service, particularly in sectors like finance and healthcare.

• Hybrid Cloud: A computing environment that combines on-premises private clouds with public cloud services, allowing data and applications to be shared between them. This model provides greater flexibility and optimization of existing infrastructure to meet fluctuating demands for storage and processing.

• Edge Computing: A distributed computing paradigm that processes data near its source rather than relying solely on centralized data centers. This is particularly important for low-latency applications, such as those involving IoT devices and real-time data analytics.

• Predictive Maintenance: A maintenance strategy that uses data analysis and AI to predict when equipment is likely to fail, allowing for maintenance to be performed just in time to prevent unexpected breakdowns. This approach aims to minimize downtimes and extend the lifespan of hardware.

• CPU (Central Processing Unit): The primary component of a computer that performs most of the processing inside a data center. CPUs are general-purpose processors that are effective for a wide range of tasks but may not match GPUs for performance in parallel processing tasks, especially in AI applications.

• Sustainability in Data Centers: The practice of managing data center operations to minimize environmental impact, particularly regarding energy consumption and carbon emissions. As of 2025, many organizations are setting ambitious sustainability targets, such as operating on carbon-free energy, due to increased regulatory and public scrutiny.

• Zero-Trust Security: A security model that requires strict identity verification for everyone attempting to access resources, regardless of whether they are inside or outside the network perimeter. As cyber threats evolve, this model is increasingly adopted to enhance protection against both external and internal risks.

• Automation in Data Centers: The use of technology to perform tasks with minimal human intervention, enhancing efficiency and operational responsiveness. In data centers, automation allows for dynamic adjustments to resource allocation and task scheduling, crucial for managing fluctuating workloads effectively.

• TPU (Tensor Processing Unit): A specialized hardware designed by Google to accelerate machine learning applications, particularly deep learning workloads. TPUs optimize the processing of tensor computations, making them more efficient than traditional GPUs for certain AI tasks.

• Supply Chain Dynamics: The complex network of production and distribution processes involved in delivering products or services. In 2025, the GPU market faces significant challenges due to previous supply chain disruptions, influencing access and pricing critical for AI development.

Source Documents

• 8 Trends That Will Shape the Data Center Industry In 2025https://www.datacenterfrontier.com/cloud/article/55253151/8-trends-that-will-shape-the-data-center-industry-in-2025
• Five 9s Digitalhttps://www.five9sdigital.com/knowledge/how-data-centers-are-enabling-artificial-intelligence-ai/
• CPU vs GPU: What’s best for Machine Learning? | Aerospikehttps://aerospike.com/blog/cpu-vs-gpu/