Deploying and scaling foundation models for generative AI inference presents challenges for organizations. Teams often struggle with complex infrastructure setup, unpredictable traffic patterns that lead to over-provisioning or performance bottlenecks, and the operational overhead of managing GPU resources efficiently. These pain points result in delayed time-to-market, suboptimal model performance, and inflated costs that can make AI initiatives unsustainable at scale.
This is a companion discussion topic for the original entry at https://aws.amazon.com/blogs/machine-learning/best-practices-to-run-inference-on-amazon-sagemaker-hyperpod/