Enterprise AI with IBM Power

Designed to enable a turnkey solution for AI enterprise workloads

Read the solution brief IBM Spyre Accelerator for Power Demo
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The time for scaling AI is now

Enterprises are facing real obstacles when it comes to scaling AI across the organization, such as data and integration complexity, shortages of the right skills, security and compliance risks, and more. IBM® Power® AI offerings and IBM Spyre™ Accelerator for Power remove these barriers through full stack optimization. The result? IBM Power provides an accelerated, flexible, and secured platform designed for enterprise AI workloads.

Top 5 Reasons to Run AI Workloads on IBM Power IBM watsonx.data on Power
Deploy AI in minutes   

Deploy AI services in one click from the IBM catalog1 and move them across Power on-prem and Power Virtual Server2 with IBM Spyre™ Accelerator for Power.

Boost productivity across teams

Improve inferencing at the point of data and implement automation that helps employees focus on higher-value work

Unlock value from enterprise data

Integrate with IBM® watsonx.data®, Red Hat OpenShift AI, Red Hat AI Inference Server, and open frameworks to accelerate inferencing on large data sets.

Built in security and compliance

Security and compliance built directly into the platform for building a trustworthy foundation to scale AI.

Features  

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Pre-built AI services   

Chose from turnkey solutions that deploy in one click to simplify setup and accelerate workloads with IBM Spyre Accelerator for Power.

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Optimized inferencing platform

Run traditional and GenAI models on Power servers with Spyre, watsonx.data, OpenShift AI, and Red Hat AI Inference Server optimized for enterprise workloads.

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Accelerated infrastructure

Harness both on- and off-chip acceleration and enterprise-class infrastructure optimized for both traditional and generative AI workloads.

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Agile integration

Embed AI directly into enterprise knowledge bases. Greater than 8 million document embeddings for knowledge base integration every hour using Spyre Accelerator for Power with batch and prompt sizes of 128.3

Use Cases

IT Operations

Predict IT issues, detect and fix incidents, and forecast and plan capacity.

Healthcare

Analyze medical images and automate claims and EHR matching with digital assistants.

Banking

Detect fraud, enable anti-money laundering, and accelerate risk and underwriting processes.

Insurance

Manage claims, prevent fraud, optimize underwriting, and support customer interactions.

Client stories
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SiPH

Learn how a hospital used AI and IBM technology to streamline cancer diagnostics, improving speed, accuracy, and focus on high-risk cases.

Read the story
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M.R. Williams

Learn how a family-owned distributor quickly recovered from a ransomware attack using IBM i and Power, restoring critical operations and driving modernization with generative AI tools.

Read the story
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Hans Geis

Learn how a logistics provider used AI on IBM i and Power to cut order processing time by 80% and speed up handling 5X — boosting accuracy and customer response.

Read the story
Take the next step

Schedule a no-cost and no-risk 30-minute meeting with an IBM Power expert.

Footnotes

1. ‎AI service of the IBM-supported catalog is delivered as one or a set of containers that can be deployed with a single deployment command. The provided UI for the catalog executes such commands in the backend based on a single click within the UI page of the respective AI service.

2. Single configuration enabled by exposed industry standard APIs to decouple services at the top and the backing inferencing service for all AI services that are part of the IBM-supported catalog. Any service that requires AI inferencing capabilities can connect inferencing services that provide OpenAI API or watsonx.ai API compliant inferencing endpoints (Spyre endpoint, RH AI Inferencing Server, IBM Cloud, OpenAI, Azure, AWS, GCP, ...). Services can run either on IBM Power or on IBM Power Virtual Server.

3. Based upon internal testing running 1M unit data set with prompt size 128, batch size 128 using 1-card container.  Individual results may vary based on workload size, use of storage subsystems and other conditions.