The groundbreaking capabilities of GPT-4 and Chat-GPT have captivated both the media and business leaders. While companies are eager to integrate artificial intelligence (AI) into their operations, it’s important to ensure that the use of these technologies aligns with their end goals. Whether it’s wind tunnel simulation, electronic design validation, customized chatbots, “digital twin” complex system simulation, or other applications, AI has sparked creativity across various industries. It’s not just the outputs that are garnering attention, but also the underlying technologies—cloud, high performance computing (HPC), automation, and machine learning (ML)—that are experiencing significant growth.
The Impact of the Cloud
Leading organizations have a history of leveraging HPC and AI using specialized CPU- and GPU-based compute clusters with low-latency network and storage infrastructure. More recently, they have turned to the cloud, as public cloud vendors have made the necessary infrastructure investments and technological advances to meet increased performance demands.
Unlike previous models, where access to compute resources was controlled by job schedulers and on-premises capacity, the cloud-based model allows for near-instant access to compute resources without the need to wait. This enables users to work with clusters that precisely meet the needs of their applications, closing the performance gap between cloud and on-premises systems.
However, the success of cloud-based HPC/AI is contingent on access to software and expertise tied to elastic cloud resources that can transform base infrastructure into high-performing configurations offered by major public cloud providers.
Leveraging the Latest Cloud Innovations
New technologies available in the cloud provide researchers and data scientists with rapid access to advanced performance and capabilities. The shift to cloud-based infrastructure marks a significant achievement, making AI more accessible and scalable for innovative enterprises of all sizes.
To achieve these objectives, CIOs are seeking to align with cloud services partners that have expertise in compute infrastructure and usage discount models for various CPU and GPU instance types within public clouds.
Optimizing Infrastructure and Deployment ROI
While the outputs of AI are transforming industries, the value of AI is only as good as the breadth of training data and speed of delivering answers for users. Cloud-based deployment environments offer the potential for users to create and test both training and inference configurations based on a variety of CPUs and GPUs for specific workloads.
Conclusion
Artificial intelligence has driven innovation across industries, with its remarkable outputs in the spotlight. However, underlying technologies like cloud computing, HPC, automation, and machine learning play a crucial role in this revolution. The move to cloud-based infrastructure marks a significant milestone, making AI more accessible and scalable.
About the Author
Phil Pokorny serves as the Chief Technology Officer (CTO) for Intelligent Platform Solutions and is responsible for all aspects of leading-edge technology for the company.
Prior to joining Penguin Computing, he spent 14 years in various engineering and system administration roles with Cummins, Inc. and Cummins Electronics.
Mr. Pokorny graduated from Rose-Hulman Institute of Technology with Bachelor of Science degrees in math and electrical engineering, with a second major in computer science.
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