Amazon SageMaker launches the updated inference optimization toolkit for generative AI

Amazon SageMaker has launched updates to its inference optimization toolkit for generative AI, offering new capabilities to enhance model performance and reduce deployment complexity.

• Added speculative decoding support for Meta Llama 3.1 models, which accelerates inference process
• Introduced FP8 quantization to optimize model size and inference latency
• Enabled compilation support for TensorRT-LLM to improve model deployment performance
• Reduces model optimization time from months to hours
• Provides out-of-the-box draft models and flexible quantization options

The toolkit allows users to optimize generative AI models quickly, reduce computational costs, and improve inference speed across various model types and hardware configurations.