The most notable business trend I’ve observed recently is the advancement of artificial intelligence, which carries significant implications for all enterprises. Addressing the question of how AI can be leveraged to enhance a company’s competitiveness and profitability should be a top priority for every business leader and IT executive.
This focus on AI implementation is evident in my interactions with industry professionals, where the majority are actively integrating AI into their strategic objectives. Many of my recent meetings have centered around discussions on AI and its impact on various business aspects.
In a recent article on customer service, a prevalent use case for AI technology, I highlighted how companies are utilizing AI to enhance customer interactions, particularly in call centers, recognizing customer service as a key differentiator for businesses.
But what exactly is high-performance processing strength?
HPC – Understanding the Computing Power Required for AI
As organizations embark on their AI journeys, they are confronted with a critical yet distinct challenge related to AI—ensuring access to the necessary processing power to achieve desired outcomes using artificial intelligence.
Unlike routine tasks, AI operations often involve processing vast amounts of data, necessitating substantial computing power. Companies are realizing that their existing data centers or cloud setups may not be equipped to efficiently support AI-driven applications. Merely scaling up server infrastructure or increasing cloud resources is insufficient to meet the demanding computing requirements of AI.
High-performance computing (HPC) capabilities, particularly through interactive computing solutions, are indispensable for AI tasks. GPUs are increasingly replacing traditional CPUs in the AI landscape, offering the processing power required for complex AI computations. While businesses can acquire GPUs for on-site installation or cloud deployment, the prevailing trend leans towards on-site setups for enhanced performance.
There are two primary reasons for this preference. Firstly, reducing latency in data transfer between distant cloud data centers is crucial for AI applications dealing with massive datasets. While latency may not significantly impact daily computing tasks, it becomes a noticeable bottleneck in AI operations that demand gigabyte-level processing capabilities.
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Cost considerations pose another challenge, especially for cloud-based processing tailored for AI requirements. While smaller AI projects can operate effectively in the cloud, larger initiatives often necessitate on-site GPU infrastructure. This may involve integrating GPU clusters within existing data center setups or deploying standalone GPU clusters with network and storage connectivity akin to hyperconverged infrastructure.
For organizations with substantial computational needs or ambitious AI projects, investing in additional GPU clusters may be the most effective approach. These clusters, typically configured in groups of eight, can be scaled by connecting multiple clusters or acquiring 256 GPU-equipped “superpods” for more extensive computing demands.
While procuring GPU clusters entails a significant investment, they come equipped with essential software, high-performance networking, and streamlined operations tailored for critical AI projects.
Navigating the optimal path forward often involves consulting with experienced partners who have executed similar projects, guiding businesses towards informed decisions on AI infrastructure investments.
