List of IBM Power AI Infrastructure Customers
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Since 2010, our global team of researchers has been studying IBM Power AI Infrastructure customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased IBM Power AI Infrastructure for AI infrastructure from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using IBM Power AI Infrastructure for AI infrastructure include: Lawrence Livermore National Laboratory, a United States based Government organisation with 9563 employees and revenues of $3.25 billion, Oak Ridge National Laboratory, a United States based Government organisation with 7000 employees and revenues of $2.60 billion, Elinar, a Finland based Professional Services organisation with 20 employees and revenues of $2.0 million and many others.
Contact us if you need a completed and verified list of companies using IBM Power AI Infrastructure, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Elinar | Professional Services | 20 | $2M | Finland | IBM | IBM Power AI Infrastructure | AI infrastructure | 2017 | n/a |
In 2017, Elinar implemented IBM Power AI Infrastructure to support its ElinarAI document and unstructured-data solutions. The IBM Power AI Infrastructure deployment was positioned within the AI infrastructure category to enable automated invoice processing and GDPR data discovery across the companys European operations.
The deployment is built on IBM Power Systems using Power8 and Power9 servers and leverages Watson Machine Learning Accelerator, formerly PowerAI, for model training and inference. IBM Power AI Infrastructure at Elinar uses Watson Machine Learning Accelerator modules for model lifecycle orchestration, GPU accelerated training, and inference serving as the core AI platform components.
Elinar runs ElinarAI on this stack to automate document processing workflows, explicitly including invoice processing and GDPR and data discovery tasks in Europe. Operational coverage emphasizes unstructured data extraction pipelines, iterative model training, and production inference pipelines that feed downstream document automation processes.
The implementation accelerated model training and enabled higher automation rates for document processing as stated in vendor materials, with training and inference workloads executed on Power Systems to keep compute close to stored unstructured data. Governance activity centers on model lifecycle management and operationalizing inference for production document workflows within the small professional services environment.
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Lawrence Livermore National Laboratory | Government | 9563 | $3.3B | United States | IBM | IBM Power AI Infrastructure | AI infrastructure | 2017 | n/a |
In 2017 Lawrence Livermore National Laboratory deployed IBM Power AI Infrastructure as the AI infrastructure foundation for its Sierra system. The Sierra deployment provided high performance compute capacity to support large scale simulations and emerging AI and deep learning workflows used for national security and scientific research in the United States.
Sierra combined IBM POWER9 processors with NVIDIA GPUs at the node level, with IBM supplying the POWER9 based infrastructure and collaborating on system software and workload tuning. The IBM Power AI Infrastructure implementation centered on GPU accelerated compute, multi node POWER9 architecture, and software stacks optimized for deep learning workloads and large scale simulation pipelines.
IBM worked directly with LLNL on AI and code optimization to tune the software stack for deep learning and simulation performance, this collaboration encompassed system software tuning and application level optimization. Use of the PowerAI distribution is reasonably inferred from the POWER9 and GPU based AI deployment and is noted as an inference rather than an explicit vendor disclosure.
Operational scope focused on laboratory research groups running simulation driven science and AI experimentation, supporting national security and scientific research missions across LLNL facilities. Governance emphasized joint engineering between IBM and laboratory HPC teams to align system configuration, application tuning, and workload orchestration for sustained AI infrastructure operations.
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Oak Ridge National Laboratory | Government | 7000 | $2.6B | United States | IBM | IBM Power AI Infrastructure | AI infrastructure | 2017 | n/a |
In 2017 Oak Ridge National Laboratory deployed IBM Power AI Infrastructure as an AI infrastructure implementation at the Oak Ridge Leadership Computing Facility. The deployment underpins the Summit supercomputer delivered by IBM, using IBM POWER9 servers paired with NVIDIA GPUs to support large scale scientific and AI workloads.
The architecture centers on POWER9 compute nodes with dense GPU acceleration to enable large scale model training and high fidelity simulation workflows. Specific use of IBM PowerAI software is inferred from the deployed POWER9 and GPU configuration rather than explicitly documented, and the implementation aligns with typical AI infrastructure capabilities such as distributed training, GPU orchestration, and data parallel compute.
Operational scope covered the OLCF facility in the United States and targeted research functions across genomics, materials science, climate modeling, and drug discovery. The IBM Power AI Infrastructure was integrated into the laboratory compute fabric and research workflows through standard cluster management and job scheduling practices, enabling centralized access for lab research teams.
The configuration using IBM Power Systems with NVIDIA GPUs increased training and simulation throughput for the lab, supporting large scale AI and modeling workloads at Oak Ridge. IBM Power AI Infrastructure remains described as the applied AI infrastructure solution for Summit based on vendor and lab sources and the observable POWER9 GPU deployment.
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