Title

AWS re:Invent 2023 - Customer Keynote Fireside Chat with NVIDIA

Summary

AWS and NVIDIA have expanded their partnership to deliver advanced infrastructure for generative AI workloads using GPUs.
NVIDIA has deployed two million GPUs in AWS, equivalent to 3,000 exascale supercomputers.
New family of GPUs announced: L4, L40S, and H200, with the H200 improving large language model inference throughput by a factor of four.
AWS will be the first cloud provider to offer NVIDIA GH200 Grace Hopper superchips with multi-node NVLink.
Grace Hopper connects two processors with a chip-to-chip interconnect at one terabyte per second, allowing for a large, coherent memory space.
AWS Nitro and EFA networking enable the creation of ultra clusters, offering significant computational power in virtual GPU instances.
NVIDIA DGX Cloud is coming to AWS, providing an AI factory for advanced research and custom AI model development.
Project SEBA, NVIDIA's largest AI factory, will feature 16,384 GPUs, reducing training time and cost for large language models by half.
The collaboration aims to integrate NVIDIA's AI stack and libraries into AWS, enhancing generative AI capabilities for developers and customers.

The partnership between AWS and NVIDIA is a strategic move to dominate the cloud-based AI and machine learning market by leveraging NVIDIA's GPU technology and AWS's cloud infrastructure.
The deployment of two million GPUs and the introduction of new GPU families indicate a significant investment in high-performance computing resources, catering to the growing demand for AI and machine learning workloads.
The GH200 Grace Hopper superchips represent a technological leap in processor interconnectivity and memory coherence, which could revolutionize how complex AI models are trained and deployed.
The NVIDIA DGX Cloud on AWS and Project SEBA highlight the trend towards cloud-based AI 'factories' where companies can train and deploy AI models at scale without the need for on-premises infrastructure.
The focus on generative AI and large language models suggests that AWS and NVIDIA are betting on these technologies to drive the next wave of innovation and business applications in the AI space.
The integration of NVIDIA's AI stack and libraries into AWS could simplify the development process for AI applications, making cutting-edge AI tools more accessible to a broader range of developers and businesses.