As a deep learning developer, data scientist, or machine learning engineer, you can choose from multiple Amazon EC2 GPU instance types to meet the evolving requirements of training and deploying ML models at scale. You can access instances with different GPU memory sizes, NVIDIA GPU architectures, capabilities (precisions, TensorCores, NVLink), GPUs per instance, number of vCPUs, system memory, and network bandwidths. Whether you provision and manage the GPU instances yourself or use them in managed services, you can choose the level of abstraction you need. In this session, learn which NVIDIA GPU instance to use when and how to get the most out of them with GPU-optimized software for your workloads. This presentation is brought to you by NVIDIA, an AWS Partner. Speaker: Shashank Prasanna (AWS)
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