AWS and NVIDIA have collaborated for over 10 years to continually deliver powerful, cost-effective, and flexible GPU-based solutions for customers. These innovations span from the cloud, with NVIDIA GPU-powered Amazon EC2 instances, to the edge, with services such as AWS IoT Greengrass deployed with NVIDIA Jetson Nano modules.
Customers around the world are using AWS and NVIDIA solutions for machine learning (ML), virtual workstations, high performance computing (HPC), and IoT services. Amazon EC2 instances powered by NVIDIA GPUs deliver the scalable performance needed for fast ML training, cost-effective ML inference, flexible remote virtual workstations, and powerful HPC computations. At the edge, customers can use AWS IoT Greengrass to extend a wide range of AWS cloud services to NVIDIA-based edge devices so the devices can act locally on the data they generate.
GPU instances for fast ML training and cost-effective inferences
For data scientists, researchers, and developers who need to speed up ML training, Amazon EC2 P4d instances deliver the highest performance for ML training in the cloud. P4d instances are powered by the latest NVIDIA A100 Tensor Core GPUs and deliver industry-leading high throughput and low latency networking. In addition, for more customized ML training, previous generation Amazon EC2 P3 instances offer several instance sizes and can deliver up to one petaflop of mixed-precision performance per instance with up to 8 NVIDIA V100 Tensor Core GPUs. Complementing P4d and P3 instances, for ML inference, Amazon EC2 G4 instances featuring NVIDIA T4 Tensor Core GPUs deliver the most cost-effective GPU instances in the cloud for ML inference.
Adapt your workforce and access creative talent across the globe
Virtual workstations on AWS enable studios to take on bigger projects, work from anywhere, and pay only for what they need. Running on Amazon EC2 G4 instances powered by NVIDIA T4 Tensor Core GPUs, virtual workstations employ the power of NVIDIA Quadro technology, the visual computing platform trusted by creative and technical professionals.
High Performance Compute
Solve large computational problems and gain new insights
Amazon EC2 P4d instances powered by NVIDIA A100 Tensor Core GPUs are an ideal platform to run engineering simulations, computational finance, seismic analysis, molecular modeling, genomics, rendering, and other GPU compute workloads. High performance computing (HPC) allows scientists and engineers to solve these complex, compute-intensive problems. HPC applications often require high network performance, fast storage, large amounts of memory, high compute capabilities, or all of the above. AWS enables customers to increase the speed of research and reduce time-to-results by running HPC in the cloud and scaling to larger numbers of parallel tasks than would be practical in most on-premises environments.
Internet of Things
Seamlessly extend AWS to edge devices so they can act locally
AWS IoT Greengrass seamlessly extends AWS to edge devices such as NVIDIA Jetson devices so they can act locally on the data they generate, while still using the cloud for management, analytics, and durable storage. With AWS IoT Greengrass, NVIDIA Jetson devices can run AWS Lambda functions, Docker containers, or both; execute predictions based on ML models; keep device data in sync; and communicate with other devices securely – even when not connected to the Internet.
Learn about how to integrate NVIDIA DeepStream on Jetson Modules with AWS IoT Core and AWS IoT Greengrass.
Aon Pathwise (P3)
PathWise uses Amazon EC2 to model customer data hundreds of times faster than legacy solutions.
Sway (P3 and G4)
Subtle Medical (P3)
AI-based PET, MRI scans bring life-saving technology to more patients via the AWS Cloud.
“Our customers rely on us to deliver highly accurate 3D Reality Models computed from multi-angle aerial photography across massive coverage areas. We use around 870 thousand GPU cores per day. We used to run this pipeline on Amazon EC2 G2 instances but switched to Amazon EC2 G4 instances and reduced our costs by 67%.”
– John Corbett, Director of Vision Systems