What is data, training and inference?

Data, training, and inference solutions combine hardware and software to create an IT system vital to handling AI workloads.

Data solutions

  • Focus on large data workloads
  • Enable superior data throughput and storage capacity
  • Tackle data lakes
  • Prepare data for AI

Training solutions

  • Build, train and retrain AI models
  • Help deliver faster AI time to insights
  • Provide data and compute-intensive infrastructure
  • Allow you to learn new capabilities from existing data

Inference solutions

  • Take in new information and infer insights based on trained models
  • Apply learning capability from training to new data
  • Deploy AI into production
  • Are deployed closer to data collection than training is

Get the right infrastructure for enterprise AI

The AI era requires a whole new infrastructure to support your cutting-edge initiatives. Get the right IT set-up to become an AI leader.

Why your current IT infrastructure won't work for AI

Make sure your data center has the infrastructure needed to take full advantage of AI.

AI infrastructure needs both training and inference

Learn the difference between training and inference, and why you need dedicated resources for both forms of AI processing.

IBM is the source for IT solutions to deploy your AI applications

IBM Power Systems for AI can help enterprises realize the full potential of AI and analytics to achieve stronger data-driven decisions, access deeper insights, and develop trust and confidence.


Get accurate model results that can give you greater confidence in business decisions.


Dynamic, industry-tested and validated tools enable productivity across all of your resources, people, processors and processes.


Stay on the cutting edge of AI technology with high data throughput, AI-assisted model optimization, and the backing of IBM Research.


Build on a secure AI solution with the security of Power Systems and IBM-secured open source frameworks.

Meet the IBM Enterprise AI Servers

Power Systems LC922: The data server for AI

The IBM Power System LC922 server is engineered to meet AI data and workload requirements. It has a storage-rich design that delivers industry-leading compute to analyze and explore data, along with the vast storage capacity to contain it.

  • Up to 3.9x price-performance with popular DBs
  • Up to 120 TB of data storage
  • Superior I/O: PCIe Gen 4

Power Systems AC922: The training server for AI

The IBM Power System AC922 server can deploy deep learning frameworks and accelerated databases for AI training. Combine the innovation data scientists desire with the dependability IT requires.

  • Fast I/O - up to 5.6x more I/O throughput than x86 servers
  • 2-6 NVIDIA® Tesla® V100 GPUs with NVLink

The new IBM Power System IC922 inference server

The IBM Power System IC922 inference server is engineered to put your AI models to work and unlock business insights. It uses optimized hardware and software to deliver the necessary components for AI inference that will move you from data to insight.

IBM Watson Machine Learning Accelerator software and the Power AC922 are a winning combination

This powerful combination of software and hardware can reduce model training times, accelerate iterations and improve insights.


faster training for Caffe¹


faster Machine Learning iterations with SnapML²

Explore Lab Services for Power Systems

IBM has experienced and highly technical consultants with years of expertise available to help plan your Power Systems fueled AI infrastructure.

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Get ready for Enterprise AI by scaling your servers

AI applications require powerful processing capabilities outside the reach of standard CPUs, which means you must scale to meet those needs.

Shifting towards enterprise AI

Artificial intelligence (AI) is moving beyond the hype cycle, as more and more organizations seek to shift their strategy to adopt AI.

Bridger Pipeline uses AI and deep-learning to protect the environment

Leaks from oil pipelines can cause enormous damage to the environment. Learn how AI was applied to help discover potentially hazardous leaks before they happen.

Talk to our IBM Power System experts

Meet the experts who will get you the information you need about Power Systems, with no cost, no obligation, and no sales pitch.

Rich Shedrick profile picture

Rich Shedrick

Solutions Sales Leader, NA Cognitive Solutions for AI HPC & Analytics

Dylan Boday profile picture

Dylan Boday

Director, Offering Management, Cognitive and Scale-out Systems

Scott Soutter profile picture

Scott Soutter

Portfolio Offering Manager, PowerAI



Elinar saw the disruptive potential of AI for its enterprise content management solutions and deployed IBM Power infrastructure to become an early adopter, slashing time-to-market and winning new clients.

From here to AI podcast series

Dez Blanchfield speaks with business leaders about artificial intelligence and deep learning adoption to discover how AI leaders are planning their AI strategy.

The value of AI to enterprise business

Enterprises are using AI today to gain a competitive advantage. Learn how organizations are using AI to provide a new and more efficient level of customer care.

AI infrastructure solutions

AI infrastructure is critical in the journey to AI. From data storage, to testing and raw computing abilities, businesses need solutions enable success in AI.

AI storage solutions

Building a pipeline of data is an important aspect of any AI infrastructure. In order to support the data intensive needs of enterprise AI, companies need reliable storage solutions that are optimized from the point of data ingestion all the way to data inferencing.


¹ Results are based IBM Internal Measurements running 1000 iterations of Enlarged GoogleNet model (mini-batch size=5) on Enlarged Imagenet Dataset (2240x2240) . Power AC922; 40 cores (2 x 20c chips), POWER9 with NVLink 2.0; 2.25 GHz, 1024 GB memory, 4xTesla V100 GPU ; Red Hat Enterprise Linux 7.4 for Power Little Endian (POWER9) with CUDA 9.1/ CUDNN 7;. Competitive stack: 2x Xeon E5-2640 v4; 20 cores (2 x 10c chips) / 40 threads; Intel Xeon E5-2640 v4; 2.4 GHz; 1024 GB memory, 4xTesla V100 GPU, Ubuntu 16.04. with CUDA .9.0/ CUDNN 7. Software: IBM Caffe with LMS Source code https://github.com/ibmsoe/caffe/tree/master-lms (link resides outside ibm.com)

² 46x SnapML (link resides outside ibm.com). In a newly published benchmark, using an online advertising dataset released by Criteo Labs (link resides outside ibm.com) with over 4 billion training examples, we train a logistic regression classifier in 91.5 seconds. This training time is 46x faster than the best result that has been previously reported (https://cloud.google.com/blog/products/gcp/using-google-cloud-machine-learning-to-predict-clicks-at-scale link resides outside ibm.com), which used TensorFlow on Google Cloud Platform to train the same model in 70 minutes.

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