What's New In The Cloud? Amazon SageMaker Rolls Out New Features
Amazon SageMaker Rolls Out New Features to Boost Generative AI Inference Scaling
There are two powerful new features in SageMaker Inference that make scaling generative AI models faster and more efficient: Container Caching and Fast Model Loader.
These features help solve some of the biggest challenges when working with large language models (LLMs), making it easier to handle sudden traffic spikes and keep costs in check. By speeding up model loading and improving autoscaling, these updates help ensure that your generative AI applications stay responsive even as demand fluctuates.
With Container Caching, SageMaker reduces the time it takes to scale AI models by pre-caching container images. This means no more waiting to download them during scaling, which leads to faster scaling times for your AI model endpoints. On top of that, the Fast Model Loader streams model weights directly from Amazon S3 to your accelerator, making model loading significantly faster than the traditional methods.
Together, these new features allow you to set up smarter autoscaling policies, so SageMaker can quickly add new instances or model copies when needed, helping you maintain peak performance during traffic surges, all while controlling costs.
Both of these features are now available in all AWS regions where Amazon SageMaker Inference is offered. For more details on how to implement these capabilities, check out our documentation.
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