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

Data solutions

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

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

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

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.

Insights

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

Productivity

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

Speed

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

Security

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

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

Pairing the Power AC922 with IBM WMLA will help you train fast and learn even faster by reducing model training times, accelerating iterations and improving insights.

3.7x

faster training for Caffe¹

46x

faster Machine Learning iterations with SnapML²

IBM Power Systems AI Starter Kit: The hardware, software and support toolkit for your AI journey

The IBM AI Starter kit includes everything you need to start training models and discovering valuable insights with IBM AI servers, plus the human support to help you implement those tools, including:

  • 2 IBM Power AC922 servers
  • 1 IBM Power LC922 server
  • Watson Machine Learning Accelerator (WMLA) software
  • Five Units of IBM Systems Lab Services

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Disclaimers

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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