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February 2025
By: The Non-Von Team The rise of artificial intelligence (AI) is disrupting up industries, fuelling innovation, and changing the way organizations handle data. At the heart of this transformation are AI chips—specialized processors built to tackle the heavy lifting of modern AI applications. However, many organizations are still relying on traditional processors for their AI needs, and that’s a choice with risks. From edge computing failures to data bottlenecks, inefficient operations, and even power outages from overtaxed hardware, sticking with outdated processors is becoming more problematic every day. 1. Failed Edge Computing: A Roadblock to Real-Time DecisionsEdge computing brings data processing closer to the source—whether that’s an autonomous vehicle, an industrial machine, or an IoT device. This setup enables quick decision-making right where the data is being generated, without relying on distant servers. However, traditional CPUs and GPUs often fall short when it comes to the low-latency and power efficiency that edge applications demand. Without AI chips, systems can face: ● Higher latency: Traditional processors struggle with the parallel processing needed for real-time tasks, causing delays in critical decisions (Google + Deloitte Impact Report). ● Increased failure rates: Many edge devices can’t handle continuous computation on older processors, leading to downtime during important operations (Google + Deloitte Impact Report). These limitations show just how outdated hardware can hold back edge devices, potentially causing costly errors or system failures. Upgrading to AI chips ensures the real-time performance needed for those critical edge applications to run smoothly. 2. Stranded Data: The Untapped ResourceWith traditional processors, the sheer volume of data generated in modern systems often overwhelms the hardware’s ability to process it. This leads to stranded data—valuable information that remains unanalysed due to insufficient computational capacity. ● Data Generation Outside Traditional Data Centers: Analysts predict that by 2025, a significant portion of enterprise-generated data will be created and processed outside traditional centralized data centers or clouds. This shift underscores the growing importance of edge computing in handling the increasing volume of data generated by enterprises (Comarch) ● Impact on Industries Like Healthcare: The healthcare sector, among others, is expected to benefit from real-time data analytics enabled by advanced processing capabilities. AI algorithms can analyse electronic health records (EHRs) and genomic data to select the most suitable patients for trials, thereby increasing the success rate of the trials. (MDPI) As data generation continues to expand outside traditional data centers, industries like healthcare stand to gain immensely from the power of edge computing. The ability to process data in real time will not only reduce the burden on centralized systems but also enable more informed, timely decision-making. 3. Inefficient Operations: Ballooning CostsAs AI workloads continue to grow, relying on traditional processors like GPUs and CPUs is leading to operational inefficiencies. Organizations are facing rising energy consumption and maintenance costs as a result. These inefficiencies put a strain on resources and make it harder to scale and maintain sustainability. ● Energy Consumption: Traditional GPUs and CPUs use a lot more power than specialized AI accelerators. For example, AMD's MI250 accelerators pull 500W of power, with peaks reaching up to 560W, while the MI300x uses a hefty 750W (Forbes). ● Over-Provisioning: To meet performance demands, organizations often allocate more resources than needed—this practice is called over-provisioning. It leads to higher costs, especially in cloud environments where it can result in significant overspending (The Register). By switching to AI-specific chips, organizations can cut down on energy consumption, lower operational costs, and set themselves up for long-term efficiency and scalability. 4. Blackouts Due to Power ConsumptionThe rapid growth of artificial intelligence is driving up energy consumption, especially when it comes to training large AI models. Traditional processors, like GPUs, are major contributors to this energy demand, which has significant environmental consequences. ● Carbon Emissions: A study from the University of Massachusetts Amherst found that training a single large AI model can emit over 626,000 pounds of CO₂—roughly equivalent to the lifetime emissions of five cars (MIT Technology Review). ● Data Center Vulnerabilities: Data centers using outdated hardware are increasingly vulnerable to power outages and system failures. This is due to surges in energy consumption during heavy AI processing tasks. A report even predicts that U.S. data center power usage could nearly triple by 2028, highlighting the strain on existing infrastructure (Reuters). By implementing specialized AI chips, like Non-Von’s, organizations can improve power efficiency, reduce their environmental impact, and boost the reliability of their data center operations. The Future of AI Hinges on Specialized ChipsThe risks of sticking with traditional processors instead of switching to AI chips are simply too big to overlook. Failed edge computing, stranded data, inefficient operations, and blackouts aren’t just technical problems—they’re business-critical issues that can throw entire industries off track. Organizations that make the switch to AI chips now will gain a competitive edge, driving operational efficiency, cutting costs, and securing a more sustainable future. Sources:
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