When you deploy a large language model (LLM) for inference on Amazon SageMaker HyperPod, there’s a gap between when you request a pod and when it’s ready to serve traffic. This gap is dominated by two sequential downloads: the inference server container image from Amazon Elastic Container Registry (Amazon ECR), and the model weights from your... Weiterlesen
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Reduce inference cold starts on Amazon SageMaker HyperPod with model caching
When you deploy a large language model (LLM) for inference on Amazon SageMaker HyperPod, there’s a gap between when you request a pod and when it’s ready to ser…