EDITOR ’ S QUESTION
MICHAEL MCNERNEY , SENIOR VICE PRESIDENT
MARKETING AND NETWORK SECURITY , SUPERMICRO
While AI is not a new concept , recent technological advancements have required a shift in how businesses across various industries manage their workloads . This evolution has empowered organizations to tackle complex computational challenges and enhance the efficiency of more straightforward tasks . However , data centre operators face mounting pressure to adapt their infrastructure .
AI technologies , ranging from natural language processing to ML models , have transformed traditional data centre configurations , enabling them to meet business demands better . The growing reliance on AI necessitates substantial new views of the infrastructure needed in compute , storage and networking resources to accommodate vast data processing and SLA requirements .
To address these challenges effectively , data centre operators must focus on developing more robust solutions and maintaining the balance of their systems , which could lead to bottlenecks .
However , transitioning to AI-specific hardware involves more than merely replacing CPUs with GPUs . Operators must also consider the heightened power and cooling demands associated with more powerful hardware . AI workloads significantly increase power consumption , prompting data centres to understand how to cool these systems efficiently .
Additionally , operators must ensure that their infrastructure is scalable . AI workloads can grow rapidly , often exponentially , as data accumulates and models are refined . Achieving scalability may involve adopting modular data centre designs or deploying disaggregated infrastructure that allows compute , storage and networking resources to scale independently .
AI compute workloads differ from traditional applications in their requirement for vast amounts of data . ML models necessitate training on extensive datasets , often comprising terabytes or even petabytes of information . This creates a pressing need for more effective data storage solutions in terms of capacity and performance .
One of AI workloads ’ most pressing challenges is the demand for specialized and accelerated computing hardware . Traditional CPUs are increasingly inadequate for AI models ’ massive parallel processing needs , particularly in training deep learning algorithms . Consequently , data centres are increasingly adopting hardware accelerators such as graphics processing units ( GPUs ), tensor processing units ( TPUs ) and other AI-specific chips . These specialized processors are designed to meet the high throughput and low-latency requirements of AI workloads .
Traditional storage architectures , such as spinning disk systems , are unlikely to meet the demands of AI workloads . As a result , many data centres are transitioning to high-performance storage solutions based on solid-state drives ( SSDs ) and nonvolatile memory express ( NVMe ) protocols . These technologies provide low latency , high-throughput performance essential for feeding data to AI models at the required speed .
Storage systems for AI workloads must be highly flexible . Operators must accommodate the
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