Multi-model endpoints (MMEs) are a powerful feature of Amazon SageMaker designed to simplify the deployment and operation of machine learning (ML) models. With MMEs, you can host multiple models on a single serving container and host all the models behind a single endpoint. The SageMaker platform automatically manages the loading and unloading of models and scales resources based on traffic patterns, reducing the operational burden of managing a large quantity of models. This feature is particularly beneficial for deep learning and generative AI models that require accelerated compute. The cost savings achieved through resource sharing and simplified model management makes SageMaker MMEs an excellent choice for you to host models at scale on AWS.

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Learn how to accelerate AI models on GPU using Amazon SageMaker multi-model endpoints with TorchServe and how it can save up to 75% on inference costs. Discover the benefits of hosting generative AI models on SageMaker MMEs and how they simplify model management.

Accelerate AI models on GPU using Amazon SageMaker multi-model endpoints with TorchServe, saving up to 75% on inference costs