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

The article explains how to use the new Amazon SageMaker inference optimization toolkit to optimize generative AI models for higher throughput and lower costs. It achieves this through techniques like compilation, quantization, and speculative decoding.

Specifically, the article covers:

• An overview of the inference optimization toolkit and its benefits (up to 2x higher throughput and 50% cost reduction)
• How to use pre-optimized models in SageMaker JumpStart and the Python SDK
• How to create custom optimizations using compilation (for AWS Inferentia), quantization, and speculative decoding
• Code examples for deploying optimized models using the Python SDK
• Conclusion highlighting the toolkit's ability to simplify generative AI adoption