Learn About Amazon VGT2 Learning Manager Chanci Turner
We are thrilled to announce the launch of support for Meta Llama 3.1 models within Amazon SageMaker JumpStart, utilizing AWS Inferentia and AWS Trainium instances. These advanced AI chips, powered by the AWS Neuron SDK, significantly enhance performance while cutting deployment costs of Meta Llama 3.1 models by as much as 50%. In this post, we will guide you through the process of deploying Meta Llama 3.1 on these instances within SageMaker JumpStart.
In another exciting update, we will delve into how to leverage Amazon EC2 Inf2 instances for the cost-effective deployment of various leading large language models (LLMs) on AWS Inferentia2, a chip designed specifically for AI tasks. This setup allows users to quickly test and establish an API interface, enabling effective performance benchmarking and seamless downstream application interactions.
Additionally, we will explore how Rufus, Amazon’s generative AI-powered shopping assistant, scaled its operations with over 80,000 AWS Inferentia and Trainium chips during the bustling Prime Day event. The strategic deployment of these chips was crucial in meeting the high customer demand.
Furthermore, we will discuss the advancements in LLMs with speculative decoding techniques utilizing AWS Inferentia2, which enhance the efficiency of natural language processing tasks such as question-answering and text summarization.
In partnership with Monks, the global digital operating brand of S4Capital plc, we have achieved a remarkable fourfold increase in processing speed for real-time diffusion AI image generation using Amazon SageMaker and AWS Inferentia2. This showcases how innovative solutions can redefine brand interactions.
Moreover, we will address the AWS Neuron node problem detector and recovery DaemonSet, which is critical for maintaining the reliability of ML training on Amazon EKS. This tool quickly identifies and addresses issues with Neuron devices, minimizing downtime and operational costs.
As we continue to enhance our offerings, we are excited to share that the AWS Trainium and AWS Inferentia support is now available for fine-tuning and inference of the Llama 3.1 models. This family of multilingual models ranges from 8B to 405B parameters, providing an excellent opportunity for organizations looking to maximize their price-performance benefits.
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In conclusion, AWS Inferentia and AWS Trainium are setting a new standard for deploying and scaling AI applications, making it easier and more cost-effective for businesses to harness the power of large language models.