“We're seeing significant interest in these chips, and we've gone back to our manufacturing partners multiple times to produce much more than we'd originally planned.”

In context

“rapidly. At the bottom layer, which is for model builders, we were the first major cloud provider to offer NVIDIA's H200 GPUs through our EC2 P5e instances. And thanks to our networking innovations like Elastic Fabric adapter and Nitro, we continue to offer advantaged networking performance. And while we have a deep partnership with NVIDIA, we've also heard from customers that they want better price performance on their AI workloads. As customers approach higher scale in their implementations, they realize quickly that AI can get costly. It's why we've invested in our own custom silicon in Trainium for training and Inferentia for inference. The second version of Trainium, Trainium2 is starting to ramp up in the next few weeks and will be very compelling for customers on price performance. We're seeing significant interest in these chips, and we've gone back to our manufacturing partners multiple times to produce much more than we'd originally planned. We also continue to see increasingly more model builders standardize an Amazon SageMaker, our service that makes it much easier to manage your AI data, build models, experiment, and deploy to production. This team continues to add features at a rapid clip punctuated by SageMaker's unique hyperpod capability, which automatically splits training workloads across more than 1,000 AI accelerators, prevents interruptions by periodically saving checkpoints, and automatically repairing faulty instances from their last saved checkpoint and saving training time by up to 40%. At the middle layer where teams want to leverage an existing foundation model customized with their data and then have features to deploy high-quality Generative AI applications, Amazon Bedrock has the broadest selection of lea”