AI changes mainframe networking because it changes where computation happens—and therefore which data must move, where it must go, and how quickly it needs to get there. In-transaction inference can run close to mainframe data and transaction flows, potentially reducing the need to copy records to a separate inference system. Larger generative AI workloads may instead need added accelerator capacity and access to unstructured data, storage, or hybrid-cloud services. Neither approach eliminates networking, and IBM’s design goals should not be mistaken for independently verified performance outcomes.
Why AI changes the data path
A conventional transaction flow sends a request to an application, accesses data, and returns a result. Adding AI introduces another decision: where inference runs. If the model runs on a separate system or remote service, the application may need to send it transaction details or derived features and receive a result. That creates an additional data path, with potential implications for latency, copying, security boundaries, and operations.
When inference runs on the mainframe near the transaction and its data, that particular export path may be reduced. This is an architectural possibility, not a guarantee: the result depends on the application, data preparation, model, and deployment. It also does not mean all AI work belongs on a mainframe. Workloads with larger models, substantial unstructured inputs, or other accelerator requirements may call for a different placement or a hybrid design.
What IBM’s Telum II changes—and what it does not establish
Inference close to transaction processing
IBM positions the Telum II on-chip AI coprocessor for inference in transaction flows, including small language models below 8 billion parameters. IBM describes this placement as a way to reduce latency. Keeping inference close to where relevant data resides can also avoid sending every transaction or feature set to a separate AI endpoint, depending on how an application is built. These are IBM’s stated design goals and architectural rationale, not an independent measurement of application-level latency or data reduction. IBM’s April 2025 Telum II and z17 product data sheet describes the platform and its intended use.
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A DPU aimed at I/O protocols
Telum II also includes a coherently attached data processing unit (DPU). IBM says it is engineered to accelerate complex input/output protocols for networking and storage on the mainframe. That describes an intended change to I/O handling; it is not evidence of a particular network-throughput gain or lower end-to-end latency for every workload. The actual path still depends on the application, storage arrangement, connectivity, and system configuration.
When generative AI needs a different data path
IBM presents Spyre as additional AI compute for generative use cases, including work with unstructured data such as text. IBM Redbooks describes on-platform applications such as assistants, document processing, information search, and extraction. Keeping processing on-platform may help avoid moving some data to a separate inference environment, but it does not make the surrounding network unnecessary: users, applications, storage, external services, and hybrid-cloud components may still need to communicate. IBM Redbooks’ AI on IBM Z applications and examples describes these deployment patterns.
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IBM’s z17 announcement said general availability began June 18, 2025, and stated that Spyre was expected to be available from Q4 2025. Those are announcement-era dates, not confirmation of a particular current configuration or orderability. Verify availability and supported options for the target system before planning a deployment. IBM’s z17 announcement provides the original timing.
IBM’s published inference figures need context
IBM reported the following z17 and z16 results in 2025. Both are extrapolations from IBM internal testing using a synthetic credit-card fraud-detection model; they are not general-purpose workload guarantees or a controlled comparison of otherwise identical systems.
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| System and IBM-reported figure | Conditions and limits stated by IBM |
|---|---|
| z17: up to 450 billion inference operations per day at 1 ms response time | Extrapolated from internal testing using a synthetic fraud-detection model, a single inference thread, batch size 160, and identified LPAR configurations. IBM says results may vary. |
| z16: up to 300 billion inference requests per day at 1 ms response time | Extrapolated from internal testing using a synthetic credit-card fraud model and configuration details different from the z17 setup. It is not a controlled general benchmark against the z17 result. |
The terms “operations” and “requests” are not interchangeable evidence of equivalent work, and the different test configurations prevent treating the two figures as a like-for-like measure. IBM’s z17 announcement and performance qualifications provide the stated figures and methodology context.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose connectivity by the path the workload actually needs
AI does not replace the existing mainframe connectivity design. It changes the mix of traffic and the importance of particular paths: transaction and inference requests, access to storage and unstructured data, communication with clients or other platforms, and—where applicable—links to hybrid-cloud services. IBM’s IBM Z Connectivity Handbook, updated July 9, 2026, covers the following connectivity areas. Its overview identifies options but is not enough by itself to select a configuration for a specific workload.
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| Connectivity area | Planning question |
|---|---|
| Fibre Channel | How will the workload reach its storage, and what storage path and I/O demand must the design support? |
| IBM zHyperLink Express | Does the workload have a latency-sensitive access requirement that makes this option relevant? |
| IBM Open Systems Adapter and Network Express Adapter | What network connectivity does the application need, and how will the adapter fit the target system and existing topology? |
| Shared Memory Communications | Can the participating systems and applications use a shared-memory communication path, and is it suitable for their traffic? |
| HiperSockets | Can communication between workloads on the platform use this internal connectivity option rather than an external network path? |
| Coupling links and common time | Do the workload and system arrangement require coupling connectivity or coordinated time? |
| Extended-distance solutions | What distance and site-to-site requirements must the design accommodate? |
| Cryptographic connectivity | What security and cryptographic requirements apply to data moving between components? |
Evaluate the relevant options against latency needs, bandwidth and I/O demand, distance, coexistence with current systems, and operational and security constraints. Confirm compatibility and supported choices for the exact machine configuration; the handbook’s category list does not establish a universal best design.
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A practical way to place AI without assuming traffic disappears
- Map the transaction and data path. Identify where transaction records and derived features are read, where inference runs, and which systems receive inputs and outputs.
- Separate inference types. Decide whether the need is low-latency inference alongside transactions, or a generative workflow that needs additional accelerator capacity and unstructured inputs.
- Identify the data that still has to move. Include storage access, application and client traffic, other platforms, and any hybrid-cloud services. A local inference path can reduce one export path without removing these connections.
- Match connectivity to workload and topology. Assess latency, I/O volume, bandwidth, distance, existing infrastructure, and security requirements against the available connectivity areas.
- Validate the target configuration and outcome. Check current hardware support and availability, then measure the application’s actual end-to-end behavior. Vendor design descriptions and synthetic benchmark extrapolations are not substitutes for results on the intended workload.
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