What AWS resource helps accelerate and reduce the cost of running deep learning inference on CPU-based instances?

AWS Elastic Inference is the correct answer because it is specifically designed to accelerate deep learning inference workloads by allowing developers to attach low-cost GPU-powered instances to CPU-based Amazon EC2 instances. This capability helps to significantly enhance the performance of deep learning models during inference while also reducing the overall costs associated with running GPU instances continuously.

By using AWS Elastic Inference, you can efficiently allocate GPU resources only when necessary, as it offers flexibility to scale and optimize costs based on the workload requirements. This resource is particularly valuable for applications that do not need a full GPU instance for every inference request, thus providing a cost-effective and efficient solution for those needing to run deep learning inference.

In contrast, AWS Lambda is an event-driven, serverless computing platform that is suitable for short-lived tasks but is not focused specifically on deep learning inference optimization. AWS Batch facilitates efficient batch computing, allowing job queuing and scheduling, but does not inherently provide the acceleration of deep learning inference on CPU instances. Lastly, AWS CloudFormation is a service for creating and managing AWS resources configurations and is not directly related to optimizing inference performance for deep learning models.