In this comprehensive video, AWS Machine Learning specialist Emily Webber explores various options for deploying foundation models on AWS, focusing on Amazon SageMaker. She covers online, offline, queued, embedded, and serverless application types, explaining their tradeoffs. The video demonstrates how to host distributed models across multiple accelerators and optimize performance through techniques like model compression. Emily provides a hands-on walkthrough of deploying a 175 billion parameter BLOOM model using SageMaker's large model inference container. She discusses key concepts like tensor parallelism and offers practical tips for efficient model deployment and serving. The video concludes with a demo of invoking the deployed model for inference.