IBM z17: The age of AI on the mainframe has arrived. Are you ready to meet it?

Lisa Dyer
SVP, Mainframe Line of Business, Ensono
So, has it happened yet? Have your leaders asked how you plan to leverage AI to enhance the applications or utilize the data that’s on your mainframe? If not, it may not be long before they ask you what’s possible. Fortunately, with the IBM z17, a whole lot could be possible. The IBM z17 is set to revolutionize the way organizations leverage artificial intelligence.
Starting with the hardware, with the Tellum II chip boosting ML inferencing throughput by 40%, and the all-new IBM Spyre chip designed specifically for generative AI models, the newest generation of the Z Platform offers ground-breaking approaches to mixed-mode AI innovation, right next to the customer data and operational data originating on mainframe.
Further, AI permeates the operating system, the core transactional and database systems, and the end-user facilities to interact with the platform.
Let’s explore some of the benefits offered by the z17:
- Process massive amounts of mainframe data… on the mainframe
One of the key advantages of the mainframe is its ability to keep customer data securely on the mainframe. Traditionally, businesses have tended to move data off the mainframe for processing, and this can carry inefficiencies and increased risks. With the z17 processing power for machine-learning and now generative AI use cases, you can process vast amounts customer and operational data where it originates, giving you faster, more processing-efficient value generated from your data than ever before. As a result, the z17 platform offers organizations new art of the possible as they look to optimize their data and AI strategies in service of their customers and their business.
- Move the needle on sustainability
The IBM z17 is not just about cutting-edge hardware and software capabilities. It’s also about sustainability. The platform’s energy-efficient design, including the Spyre chip that operates at just 75 watts, significantly reduces the environmental footprint of AI operations and mission-critical workloads.
Depending on the AI use cases for the organization, executing AI models could reduce reliance on external data processing centers, which can substantially optimize cooling and power resources. Lowering data center costs and supporting corporate sustainability goals is a compelling value proposition.
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