Achieve up to ~2x higher throughput while reducing costs by ~50% for generative AI inference on Amazon SageMaker with the new inference optimization toolkit – Part 1

This article discusses Amazon SageMaker's new inference optimization toolkit that helps optimize generative AI models for better performance and cost efficiency. It enables applying techniques like speculative decoding, quantization, and compilation to achieve up to ~2x higher throughput and ~50% cost reduction.

Specifically, the article covers:

• Benefits of the inference optimization toolkit
• Speculative decoding for faster inference without accuracy loss
• Quantization for reduced memory footprint and faster decoding
• Compilation for optimal performance on target hardware
• Performance benchmarks and cost savings analysis
• Getting started with the toolkit using SageMaker JumpStart models