New accelerated computing instances feature NVIDIA T4 Tensor Core GPUs to provide the most cost-effective compute in the cloud for running machine learning inference and graphics-intensive applications
Clarifai and Electronic Arts among customers using Amazon EC2 G4 instances
SEATTLE–(BUSINESS WIRE)–#GPU–Today, Amazon Web Services, Inc. (AWS), an Amazon.com company (NASDAQ: AMZN), announced the general availability of G4 instances, a new GPU-powered Amazon Elastic Compute Cloud (Amazon EC2) instance designed to help accelerate machine learning inference and graphics-intensive workloads, both of which are computationally demanding tasks that benefit from additional GPU acceleration. G4 instances provide the industry’s most cost-effective machine learning inference for applications, like adding metadata to an image, object detection, recommender systems, automated speech recognition, and language translation. G4 instances also provide a very cost-effective platform for building and running graphics-intensive applications, such as remote graphics workstations, video transcoding, photo-realistic design, and game streaming in the cloud. To get started with G4 instances visit https://aws.amazon.com/ec2/instance-types/g4.
Machine learning involves two processes that require compute – training and inference. Training entails using labeled data to create a model that is capable of making predictions, a compute-intensive task that requires powerful processors and high-speed networking. Inference is the process of using a trained machine learning model to make predictions, which typically requires processing a lot of small compute jobs simultaneously, a task that can be most cost-effectively handled by accelerating computing with energy-efficient NVIDIA GPUs. With the launch of P3 instances in 2017, AWS was the first to introduce instances optimized for machine learning training in the cloud with powerful NVIDIA V100 Tensor Core GPUs, allowing customers to reduce machine learning training from days to hours. However, inference is what actually accounts for the vast majority of machine learning’s cost. According to customers, machine learning inference can represent up to 90% of overall operational costs for running machine learning workloads.
New G4 instances feature the latest generation NVIDIA T4 GPUs, custom 2nd Generation Intel Xeon Scalable (Cascade Lake) processors, up to 100 Gbps of networking throughput, and up to 1.8 TB of local NVMe storage, to deliver the most cost-effective GPU instances for machine learning inference. And with up to 65 TFLOPs of mixed-precision performance, G4 instances not only deliver superior price/performance for inference, but also can be used cost-effectively for small-scale and entry-level machine learning training jobs that are less sensitive to time-to-train. G4 instances also provide an ideal compute engine for graphics-intensive workloads, offering up to a 1.8x increase in graphics performance and up to 2x video transcoding capability over the previous generation G3 instances. These performance enhancements enable customers to use remote workstations in the cloud for running graphics-intensive applications like Autodesk Maya or 3D Studio Max, as well as efficiently create photo-realistic and high-resolution 3D content for movies and games.
“We focus on solving the toughest challenges that hold our customers back from taking advantage of compute intensive applications,” said Matt Garman, Vice President, Compute Services, AWS. “AWS offers the most comprehensive portfolio to build, train, and deploy machine learning models powered by Amazon EC2’s broad selection of instance types optimized for different machine learning use cases. With new G4 instances, we’re making it more affordable to put machine learning in the hands of every developer. And with support for the latest video decode protocols, customers running graphics applications on G4 instances get superior graphics performance over G3 instances at the same cost.”
Customers with machine learning workloads can launch G4 instances using Amazon SageMaker or AWS Deep Learning AMIs, which include machine learning frameworks such as TensorFlow, TensorRT, MXNet, PyTorch, Caffe2, CNTK, and Chainer. G4 instances will also support Amazon Elastic Inference in the coming weeks, which will allow developers to dramatically reduce the cost of inference by up to 75% by provisioning just the right amount of GPU performance. Customers with graphics and streaming applications can launch G4 instances using Windows, Linux, or AWS Marketplace AMIs from NVIDIA with NVIDIA Quadro Virtual Workstation software preinstalled. A bare metal version will be available in the coming months. G4 instances are available in the US East (N. Virginia, Ohio), US West (Oregon, N. California), Europe (Frankfurt, Ireland, London), and Asia Pacific (Seoul and Tokyo) Regions, with availability in additional regions planned in the coming months. G4 instances are available to be purchased as On-Demand, Reserved Instances, or Spot Instances.
Clarifai is a leading artificial intelligence company that excels in visual recognition to solve real-world challenges. “We apply machine learning to image and video recognition, helping customers better understand their media assets and apply it across a broad set of applications, such as providing personalized online shopping experience or measuring in-store shopper behaviors,” said Robert Wen, Head of Engineering at Clarifai. “We provide our customers with a full-featured API that allows them to utilize our pre-trained machine learning models and make predictions on their data. G4 instances offer a highly cost-effective solution that will enable us to make it more economical for our customers to use AI across a broader set of use cases.”
Electronic Arts (EA) is a global leader in digital interactive entertainment, delivering games, content, and online services to hundreds of millions of players around the world through Internet-connected consoles, mobile devices, and personal computers. “Leveraging the power of the cloud with providers such as Amazon Web Services has revolutionized how we create games and how players experience them,” said Erik Zigman, EA’s Vice President of Cloud, Social, Marketplace, and Cloud Gaming Engineering. “Working with AWS’s G4 instance has enabled us to build cost-effective and powerful services that are optimized for bringing online gaming to a wide range of devices.”
GumGum is an artificial intelligence company with deep expertise in computer vision. “We use our proprietary computer vision technology to identify content relevant to marketers to deliver highly visible advertising campaigns and rich insights to brands and agencies,” said Brian Fuller, Engineering Manager, at GumGum. “GumGum scans millions of images and videos each day across the web, social media, and broadcast television using AI. The new Amazon EC2 G4 instances provide us with the ideal balance of price and performance, allowing us to optimize our content processing pipelines, lower our costs to generate data insights, and provide our clients the ability to precisely target audiences and deliver contextually relevant advertising.”
PureWeb’s interactive streaming technology enables users to publish, collaborate, and interact with massive data files, including photo-real 3D simulations and game engine projects. “Our Reality product, deployed on AWS, is a fully managed, secure, and scalable service that provides on-demand access to 3D photorealistic renderings built using Unity or Unreal Engine,” said Barry Allen, CEO, PureWeb. “With their low cost and latest NVIDIA T4 GPUs, AWS G4 instances are perfect for our graphics-intensive workloads, as they provide the right balance of performance and cost, allowing us to stream at scale to anyone on any device.”
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