Tag: Channy Yun (윤석찬)

As we preannounced at AWS re:Invent 2024, you can now use Stable Diffusion 3.5 Large in Amazon Bedrock to generate high-quality images from text descriptions in a wide range of styles to accelerate the creation of concept art, visual effects, and detailed product imagery for customers in media, gaming, advertising, and retail. In October 2024, Stability AI introduced Stable Diffusion 3.5 Large, the most powerful model in the Stable Diffusion family at 8.1 billion parameters trained on Amazon SageMaker HyperPod, with superior quality and prompt adherence. Stable Diffusion 3.5 Large can accelerate storyboarding, concept art creation, and rapid prototyping of visual effects. You can quickly generate high-quality 1-megapixel images for campaigns, social media posts, and advertisements, saving time and resources…

Today we’re announcing the general availability of Amazon Elastic Compute Cloud (Amazon EC2) U7inh instance, a new addition to EC2 High Memory family, built in collaboration with Hewlett Packard Enterprise (HPE). Amazon EC2 U7inh instance runs on the 16-socket HPE Compute Scale-up Server 3200, and are built on the AWS Nitro System to deliver a fully integrated and managed experience consistent with other EC2 instances. Powered by the fourth generation Intel® Xeon® Scalable processors (Sapphire Rapids), U7inh instance supports 32 TB of memory and 1920 vCPUs. This instance offers the highest compute performance, largest compute and memory size in the Amazon Web Services (AWS) Cloud for running large, mission-critical database workloads, like SAP HANA. In May 2024, we launched U7i…

Today, we’re announcing the general availability of Amazon SageMaker HyperPod recipes to help data scientists and developers of all skill sets to get started training and fine-tuning foundation models (FMs) in minutes with state-of-the-art performance. They can now access optimized recipes for training and fine-tuning popular publicly available FMs such as Llama 3.1 405B, Llama 3.2 90B, or Mixtral 8x22B. At AWS re:Invent 2023, we introduced SageMaker HyperPod to reduce time to train FMs by up to 40 percent and scale across more than a thousand compute resources in parallel with preconfigured distributed training libraries. With SageMaker HyperPod, you can find the required accelerated compute resources for training, create the most optimal training plans, and run training workloads across different…

Today, we’re announcing the general availability of Amazon SageMaker HyperPod flexible training plans to help data scientists train large foundation models (FMs) within their timelines and budgets and save them weeks of effort in managing the training process based on compute availability. At AWS re:Invent 2023, we introduced SageMaker HyperPod to reduce the time to train FMs by up to 40 percent and scale across thousands of compute resources in parallel with preconfigured distributed training libraries and built-in resiliency. Most generative AI model development tasks need accelerated compute resources in parallel. Our customers struggle to find timely access to compute resources to complete their training within their timeline and budget constraints. With today’s announcement, you can find the required accelerated…