Amazon SageMaker AI introduces EAGLE based adaptive speculative decoding to accelerate generative AI inference

Generative AI models continue to expand in scale and capability, increasing the demand for faster and more efficient inference. Applications need low latency and consistent performance without compromising output quality. Amazon SageMaker AI introduces new enhancements to its inference optimization toolkit that bring EAGLE based adaptive speculative decoding to more model architectures. These updates make it easier to accelerate decoding, optimize performance using your own data and deploy higher-throughput models using the familiar SageMaker AI workflow.

EAGLE, short for Extrapolation Algorithm for Greater Language-model Efficiency, is a technique that speeds up large language model decoding by predicting future tokens directly from the hidden layers of the model. When you guide optimization using your own application data, the improvements align with the actual patterns and domains you serve, producing faster inference that reflects your real workloads rather than generic benchmarks. Based on the model architecture, SageMaker AI trains EAGLE 3 or ...