Security News > 2020 > November > AWS launches Amazon EC2 P4d instances, boosting performance for ML training and HPC
Amazon Web Services announced the general availability of Amazon Elastic Compute Cloud P4d instances, the next generation of GPU-powered instances delivering 3x faster performance, up to 60% lower cost, and 2.5x more GPU memory for machine learning training and high-performance computing workloads when compared to previous generation P3 instances.
Using P4d instances with AWS's Elastic Fabric Adapter and NVIDIA GPUDirect RDMA, customers are able to create P4d instances with EC2 UltraClusters capability.
With EC2 UltraClusters, customers can scale P4d instances to over 4,000 A100 GPUs by making use of AWS-designed non-blocking petabit-scale networking infrastructure integrated with Amazon FSx for Lustre high performance storage, offering on-demand access to supercomputing-class performance to accelerate machine learning training and HPC. Data scientists and engineers are continuing to push the boundaries of machine learning by creating larger and more-complex models that provide higher prediction accuracy for a broad range of use cases, including perception model training for autonomous vehicles, natural language processing, image classification, object detection, and predictive analytics.
P4d instances are also built on the AWS Nitro System, AWS-designed hardware and software that has enabled AWS to deliver an ever-broadening selection of EC2 instances and configurations to customers, while offering performance that is indistinguishable from bare metal, providing fast storage and networking, and ensuring more secure multi-tenancy.
"The pace at which our customers have used AWS services to build, train, and deploy machine learning applications has been extraordinary. At the same time, we have heard from those customers that they want an even lower cost way to train their massive machine learning models," said Dave Brown, Vice President, EC2, AWS. "Now, with EC2 UltraClusters of P4d instances powered by NVIDIA's latest A100 GPUs and petabit-scale networking, we're making supercomputing-class performance available to virtually everyone, while reducing the time to train machine learning models by 3x, and lowering the cost to train by up to 60% compared to previous generation instances."
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