However, you are still responsible for AWS charges for all AWS resources used. On the GPU side, the A10G GPUs deliver to to 3.3x better ML training performance, up to 3x better ML inferencing performance, and up to 3x better graphics performance, in comparison to the T4 GPUs in the G4dn instances. This GPU-optimized AMI is free with an option to purchase enterprise support offered through NVIDIA AI Enterprise. The Windows Server 2019 with NVIDIA Driver AMI is provided at no cost. Today we’re excited to announce the general availability of Amazon EC2 Elastic GPUs for Windows. Blended Price Avg (OnDemand and 1yr Reserved) p3dn.24xlarge. The NGC catalog provides free access to containerized AI, Data Science, and HPC applications, pre-trained models, AI SDKs and other resources to enable data scientists, developers, and researchers to focus on building and deploying solutions. In the table below we show AWS pricing for the p3 instances. This AMI provides easy access to NVIDIA's NGC Catalog, a hub for GPU-optimized software, for pulling & and running performance-tuned, tested, and NVIDIA certified docker containers. General purpose, compute optimized, memory optimized, storage optimized, and accelerated computing instance types are available that provide the optimal compute, memory, storage, and networking balance for your workloads. These instances feature eight NVIDIA V100 Tensor Core GPUs with 32 GB of memory each, 96 custom Intel Xeon. AWS also offers the industry’s highest performance model training GPU platform in the cloud via Amazon EC2 P3dn.24xlarge instances. Using this AMI, you can spin up a GPU-accelerated EC2 VM instance in minutes with a pre-installed Ubuntu OS, GPU driver, Docker and NVIDIA container toolkit. Amazon EC2 provides the broadest and deepest instance choice to match your workload’s needs. AWS was first in the cloud to offer NVIDIA V100 Tensor Core GPUs via Amazon EC2 P3 instances. The NVIDIA GPU-Optimized AMI is a virtual machine image for accelerating your GPU accelerated Machine Learning, Deep Learning, Data Science and HPC workloads.
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