Amazon SageMaker has introduced a new inference optimization toolkit to enhance the performance of generative AI models. This toolkit offers various optimization techniques such as speculative decoding, quantization, and compilation, which can lead to significant cost reductions and improved throughput for models like Llama 3, Mistral, and Mixtral. By utilizing these techniques, users can achieve up to double the throughput and save up to 50% on costs compared to previous setups.
The inference optimization toolkit simplifies the process of applying optimization techniques to generative AI models. Users can select from a menu of techniques, create an optimization recipe, run benchmarks on custom data, and deploy models with just a few clicks. This toolkit reduces the time and resources needed for optimization, allowing users to focus on their business objectives rather than the technicalities of model optimization.
One of the key techniques supported by the toolkit is speculative decoding, which speeds up the decoding process of large language models without compromising text quality. By using a draft model to generate candidate tokens validated by a target model, speculative decoding achieves faster response generation. The toolkit also supports quantization, which reduces memory requirements and accelerates inference by using lower-precision data types. Another technique, compilation, optimizes models for specific hardware types to improve performance without sacrificing accuracy.
The toolkit provides compatibility with popular models like Llama 3 and Mistral, allowing users to deploy models with accelerated inference techniques in minutes through either the Amazon SageMaker Studio UI or the Python SDK. By leveraging techniques like speculative decoding and quantization, users can enhance model performance, reduce costs, and improve latency for use cases like question answering. Additionally, the toolkit offers features like pre-compiled artifacts to speed up model loading time on hardware like GPUs and AWS Trainium.
With the inference optimization toolkit, Amazon SageMaker aims to empower users to achieve better price-performance for generative AI models. By providing a range of optimization techniques and streamlined deployment processes, the toolkit enables users to optimize models more efficiently and effectively. Users can now leverage advanced techniques like speculative decoding, quantization, and compilation to unlock the full potential of their generative AI models on Amazon SageMaker.
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https://aws.amazon.com/blogs/machine-learning/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/
