Latest news with #Z17
Forbes
10-04-2025
- Business
- Forbes
Do Mainframes Have A Role In The AI Era?
AI-Generated, AI-Enabled Mainframe Francis Sideco A couple of years after generative AI first entered mainstream consciousness, every industry segment is attempting to leverage it to improve efficiency and offer new products and services. While training will continue to evolve and drive innovation, inferencing will drive value creation through advanced AI capabilities such as chain of thought reasoning, multi-modal functionality and multi-model support combining different types of generative AI models with predictive AI. Most of the conversations on delivering these inferencing solutions gravitate around AI data centers, edge infrastructure, and/or on-device processing. This begs the question: Does the mainframe have a role in the AI Era? What Is A Mainframe? While servers are designed more for supporting general-purpose applications and multiple clients or functions like website hosting and email servers, mainframes are designed for high-volume, mission-critical tasks such as financial transaction processing and are often used in heavily regulated industries. As such, mainframes require a higher degree of capacity, reliability and security enabled with advanced virtualization, disaster recovery, backwards compatibility and built-in redundancy. Additionally, workloads are typically handled by a centralized mainframe system whereas a distributed architecture to spread workloads over many systems is commonly used in a server architecture. With the capacity, reliability and security that mainframes provide along with their ubiquity in supporting high-volume, high-value transactions and data processing, the answer to whether mainframes have a role in the AI Era is an unequivocal 'Yes!' IBM, the leader in mainframe solutions with 70% of all global financial transactions going through their mainframes, is a prime example. A Mainframe For The AI Era Telum II By The Numbers Francis Sideco IBM recently announced the latest in its Z family of mainframes, the Z17, with the goal of addressing the needs of the AI Era while still delivering on the rigorous expectations associated with mainframes. According to IBM, this Z17 generation is powered by its 5nm 5.5GHz Telum II CPU, which, compared to the previous generation, delivers an 11% increase in single-thread performance, up to 20% capacity expansion and up to 64 TB of memory, while also doing so with up to 27% power reduction. Additionally, Telum II has an enhanced on-board AI accelerator capable of predictive and some generative AI workloads. For the generative AI workloads that require more acceleration, the Z17 can also be upgraded with the new Spyre Accelerator PCIe card. Spyre Accelerator By The Numbers Francis Sideco Based on IBMs reported performance numbers, the Z17 provides 7.5x more AI throughput than the Z16 generation delivering up to 450 billion AI inferences with 1ms response times per day. What AI Workloads Need A Mainframe? Due to their heavy use in financial transactions and mission-critical data processing, mainframes are most effective when using a combination of predictive and generative AI models. For example, because of the high-volume, central processing, and multi-model capabilities, mainframes can effectively and efficiently analyze patterns from the transactions and data passing through the system and infer conclusions that can be used in advanced fraud detection and anti-money laundering applications for improved accuracy and fewer false positives. Mainframes also combine these capabilities with mission-critical business data to help enable business, code and operations assistants to increase productivity and reduce the time needed for skills training, and autonomous agentic AI applications like automated trading and healthcare applications. Other areas where AI-enabled mainframes are being used include, but are not limited tom loan risk mitigation, insurance claims fraud detection and prevention, payments fraud, geospatial analysis, climate change impact, loan risk mitigation, cybersecurity and sentiment analysis. These are just a small subset of applications where predictive, generative and even agentic AI leverage the mainframe for business outcomes that would otherwise be inefficient or not available because of the data and/or security requirements on standard server configurations especially in heavily regulated industries in which mainframes are typically deployed. The Future Of Mainframes In The AI Era According to IBM, there are already more than 250 client-identified AI use cases on the Z mainframe platform and growing. But it's not all about the hardware. IBM leverages its other AI assets like watsonx, Granite, InstructLab and even their consulting services, across IBMs solutions, including the Z platform, positioning the company as a strong partner for the age of enterprise AI. Competitors such as Dell, Fujitsu, and Unisys are also looking to leverage AI for mainframe workloads. Next generation mainframe development typically takes 5-7 years and if they're anything like IBM, it is safe to say that the next few generations of AI-enabled mainframes are already in the works. Not only are mainframes surviving in the AI Era, they are thriving.
Yahoo
07-04-2025
- Business
- Yahoo
Pay Close Attention to This Crucial Revenue Source for Artificial Intelligence (AI) Giant IBM
When most investors think of artificial intelligence stocks, Nvidia is a top-of-mind name. And rightfully so. Its hardware is at the heart of most AI data centers. Or maybe it's Microsoft or Alphabet's, both of which offer popular AI-powered virtual assistants free of charge. IBM (NYSE: IBM), on the other hand, doesn't come up much during discussions of artificial intelligence's likely future. However, maybe it should. Smart investors would at least put the company on their AI radars anyway. Here's why. To be clear, IBM doesn't hold a candle to Nvidia's share of the AI data center market. As noted by the Motley Fool's own in-house research team, Nvidia generated more than $35 billion worth of AI data center revenue for the final quarter of last year, while Intel, Advanced Micro Devices, and IBM each only reported on the order of $4 billion. Nvidia is also the only one of these hardware makers to see any meaningful net growth of their AI data center business over the course of the past several years. Still, IBM is a budding artificial intelligence name worth watching. But first things first. IBM's single-biggest profit center these days isn't hardware. It's software, which accounts for more than 40% of its revenue and nearly two-thirds of the company's total gross profits. These numbers, however, come with an important footnote. See, it's hardware sales that ultimately generate software and consulting revenue. As the company has regularly pointed out for some time now, for every $1 spent on its cloud hardware -- which makes up most of what the company categorizes as "infrastructure" -- an additional $3 to $5 is spent on software while another $6 to $8 is shelled out on services. IBM even picks up an extra dollar or two on additional purchases of enterprise infrastructure. IBM's historical numbers bear this idea out, too. Connect the dots. Artificial intelligence data centers will never be IBM's biggest business. However, if that business grows, then the rest of IBM will grow even more -- and there's plenty of reason to believe such growth is in the cards. There's no denying that it hasn't happened yet. The company's enterprise infrastructure business is arguably stagnant, in fact, mostly weighed down by weak sales of its Z series of mainframes and its state-of-the-art Z16 model, in particular. Although far from being the kind of mainframe computer system most people think of when the label became fairly common back in the 1970s through the 1980s, it's still a legacy concept that's distinctly different from most other AI data centers that largely utilize Nvidia's hardware or power apps like Microsoft's AI tool, which is called Copilot. As time marches on, though, the best solutions always move into the mainstream. As it turns out, IBM's Z16, as well as its upcoming Z17 platform, are outstanding at a type of machine learning called inference. That won't mean much to many people, but it does matter. To date, most of the commonly used AI tools like Google's Gemini and OpenAI's ChatGPT utilize what's called a "training" approach. That just means these platforms parse a massive amount of selected text-based information on a topic and then come up with an appropriate response to a query based on the sum total of all this aggregated information. It works fine for most purposes. Inference, on the other hand, is a different approach to machine learning. With inference models, a platform considers all known information but is tasked with taking a new action or making a reasoned but unproven prediction based on this known data. As IBM explains it, "inference is an AI model's moment of truth," ultimately ending in "a test of how well it can apply information learned during training to make a prediction or solve a task." It's a small, nuanced difference, but it's also a pretty big deal to the artificial intelligence industry. Now that AI platforms effectively know what they don't know but also know they might be asked to craft an appropriate response or solution, artificial intelligence itself is quietly entering a new era. That's why industry research outfit believes the worldwide AI inference server market is set to grow at an annualized pace of more than 18% through 2034, matching up with a similar outlook from Lucintel. This prospective growth, of course, bodes very well for IBM, which specializes in the very kings of mainframe servers that are on the cusp of significant demand growth. IBM isn't the only name that is making artificial intelligence platforms capable of handling heavy-duty inference tasks, for the record. Most any company in the AI hardware business is able to optimize their tech for this relatively newer approach to machine learning. IBM is arguably one of the better inference plays, though, if not the best. The Telum II processors and their on-chip Spyre accelerators, capable of handling 24 TOPS (tera operations per second), will be found inside Z17 servers. They are the end result of a long string of successful inference-friendly technological developments that aren't quite being matched by other names in the AI hardware business. Data center operators just need to recognize their value. Then, investors need to follow suit. It's still arguably a bet worth making sooner rather than later, though, before anyone else realizes there's an impending shift ahead for how many of the artificial intelligence industry's platforms process data and respond to users' requests. Just a little more AI server revenue could generate considerably more high-margin software revenue for IBM. Most of this new revenue would also be recurring. Before you buy stock in International Business Machines, consider this: The Motley Fool Stock Advisor analyst team just identified what they believe are the for investors to buy now… and International Business Machines wasn't one of them. The 10 stocks that made the cut could produce monster returns in the coming years. 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The Motley Fool has a disclosure policy. Pay Close Attention to This Crucial Revenue Source for Artificial Intelligence (AI) Giant IBM was originally published by The Motley Fool
