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Today, we’re excited to announce container image caching for Amazon SageMaker AI inference, the next major advancement in our faster scaling optimization journey. This speeds up end-to-end latency by up to 2x for generative AI models during scale-out events. Over the years, Amazon SageMaker AI has continued to reduce latency across these scaling stages: detecting the need to scale out, provisioning instances, downloading container images, fetching model weights, and starting containers.

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As organizations scale generative AI workloads in production, securing reliable GPU compute has become one of the most persistent operational challenges. Large language models (LLMs) and multimodal architectures demand specific instance types and when that capacity isn’t available, endpoints fail before they serve a single request. Building a real-time inference endpoint on Amazon SageMaker AI has meant committing to a single instance type at creation time.