Amazon Elastic Inference Description

Amazon Elastic Inference offers a cost-effective way to enhance Amazon EC2 and SageMaker instances or Amazon ECS tasks with GPU-powered acceleration, potentially cutting deep learning inference expenses by as much as 75%. It seamlessly supports models built with TensorFlow, Apache MXNet, PyTorch, and ONNX. Inference involves predicting outcomes based on a model that has already been trained. Notably, in the realm of deep learning, inference can account for up to 90% of total operational costs due to two main factors. The first factor is that dedicated GPU instances are primarily optimized for training rather than inference; training typically involves processing numerous data samples concurrently, whereas inference often handles one input at a time in real time, leading to minimal GPU resource usage. Consequently, this results in an inefficient cost structure for standalone GPU inference. Conversely, standalone CPU instances lack the necessary specialization for matrix operations and therefore tend to be inadequate for the speed requirements of deep learning inference. By integrating Elastic Inference, users can strike a balance between performance and cost, ensuring that their inference workloads are handled more efficiently.

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Company Details

Company:
Amazon
Year Founded:
2006
Headquarters:
United States
Website:
aws.amazon.com/machine-learning/elastic-inference/

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Online Support

Amazon Elastic Inference Features and Options

Infrastructure-as-a-Service (IaaS) Provider

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Data Migration
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Load Balancing
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SLA Monitoring

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