As organizations continue to scale their use of artificial intelligence, the challenge is shifting from adoption to execution: delivering AI where it creates real business value while maintaining security, performance, and governance across mission-critical environments.
With IBM z17™, IBM brings AI directly into the enterprise core, helping organizations modernize faster, simplify operations, and strengthen resilience where it matters most, at the heart of their transactional systems.
Accelerating Growth with AI at the Core
IBM z17 embeds AI capabilities directly into the environment where core business processes already run. By reducing the distance between data and intelligence, organizations can improve decision-making speed, enable new Agentic AI services, and accelerate application modernization initiatives.
This shift reflects a broader transformation in enterprise architecture: intelligence is increasingly delivered where data is generated and governed, rather than distributed across fragmented environments.
Simplifying Operations and Strengthening Security
In increasingly hybrid and distributed IT landscapes, complexity has become one of the main barriers to agility.
IBM z17 addresses this through automation that spans systems and workloads, helping organizations reduce operational overhead, improve visibility, and make better use of existing investments. At the same time, it supports stronger cyber resilience by improving transparency and simplifying compliance and security management.
AI at Enterprise Scale
A key differentiator of IBM z17 is its ability to run AI workloads at exceptional scale within transactional environments. According to IBM performance testing, IBM z17 can process up to 450 billion inference operations per day with a 1-millisecond response time using a credit card fraud detection deep learning model. Alternatively, it can support up to 5 million inference operations per second with sub-millisecond response times using the same model.*
Built on the IBM Telum II™ processor, IBM z17 is designed to support composite AI and multi-model inferencing directly within transactional workloads, enabling real-time intelligence where business decisions are made.
Primeur's Perspective
At Primeur, we believe the real value of AI emerges when organizations can access, govern, and activate data seamlessly across the enterprise.
For more than four decades, we have supported organizations in simplifying data integration, decoupling applications, and ensuring secure data movement across complex environments. IBM z17 represents an important step forward for enterprises seeking to combine AI innovation with operational resilience.
As part of the IBM Partner Program, we continue to work alongside IBM technologies to help customers build data-driven foundations that turn AI initiatives into measurable and sustainable business outcomes.
As part of our ongoing commitment to the IBM Z ecosystem, we are also evolving our long-standing Managed File Transfer capabilities on IBM Z to support a new generation of intelligent, automated, and insight-driven data movement services. This evolution builds on trusted foundations while introducing greater levels of automation, operational visibility, and AI-enabled intelligence across data flows.
Looking Ahead
As AI becomes embedded deeper into critical business processes, enterprises require platforms that combine intelligence, security, and scalability without increasing complexity.
IBM z17 reflects this evolution, enabling organizations to bring AI closer to the core, streamline operations, and protect their most critical assets while accelerating innovation across the enterprise landscape.
*Performance results are extrapolated from IBM internal testing using IBM z17 hardware and a synthetic credit card fraud detection deep learning model.
Disclaimer: Performance result is extrapolated from IBM® internal tests running on IBM Systems Hardware of machine type 9175. The benchmark was executed with 1 thread performing local inference operations using a LSTM based synthetic Credit Card Fraud Detection (CCFD) model (https://github.com/IBM/ai-on-z-fraud detection) to exploit the IBM Integrated Accelerator for AI. A batch size of 160 was used. IBM Systems Hardware configuration: 1 LPAR running Red Hat® Enterprise Linux® 9.4 with 6 IFLs (SMT), 128 GB memory. 1 LPAR with 2 CPs, 4 zIIPs and 256 GB memory running IBM z/OS® 3.1 with IBM z/OS Container Extensions (zCX) feature. Results may vary.
