New – Amazon EC2 G5g Instances Powered by AWS Graviton2 Processors and NVIDIA T4G Tensor Core GPUs
AWS Graviton2 processors are custom-designed by AWS to enable the best price performance in Amazon EC2. Thousands of customers are realizing significant price performance benefits for a wide variety of workloads with Graviton2-based instances.
Today, we are announcing the general availability of Amazon EC2 G5g instances that extend Graviton2 price-performance benefits to GPU-based workloads including graphics applications and machine learning inference. In addition to Graviton2 processors, G5g instances feature NVIDIA T4G Tensor Core GPUs to provide the best price performance for Android game streaming, with up to 25 Gbps of networking bandwidth and 19 Gbps of EBS bandwidth.
These instances provide up to 30 percent lower cost per stream per hour for Android game streaming than x86-based GPU instances. G5g instances are also ideal for machine learning developers who are looking for cost-effective inference, have ML models that are sensitive to CPU performance, and leverage NVIDIA’s AI libraries.
G5g instances are available in the six sizes as shown below.
|Instance Name||vCPUs||Memory (GB)||NVIDIA T4G Tensor Core GPU||GPU Memory (GB)||EBS Bandwidth (Gbps)||Network Bandwidth (Gbps)|
|g5g.xlarge||4||8||1||16||Up to 3.5||Up to 10|
|g5g.2xlarge||8||16||1||16||Up to 3.5||Up to 10|
|g5g.4xlarge||16||32||1||16||Up to 3.5||Up to 10|
These instances are a great fit for many interesting types of workloads. Here are a few examples:
- Streaming Android gaming—With G5g instances, Android game developers can build natively on Arm-based GPU instances without the need for cross-compilation or emulation on x86-based instances. They can encode the rendered graphics and stream the game over the network to a mobile device. This helps simplify development efforts and time and lowers the cost per stream per hour by up to 30 percent.
- ML Inference —G5g instances are also ideal for machine learning developers who are looking for cost-effective inference, have ML models that are sensitive to CPU performance, and leverage NVIDIA’s AI If you don’t have any dependencies on NVIDIA software, you may use Inf1 instances, which deliver up to 70 percent lower cost-per-inference than G4dn instances.
- Graphics rendering—G5g instances are the most cost-effective option for customers with rendering workloads and dependencies on NVIDIA libraries. These instances also support rendering applications and use cases that leverage industry-standard APIs such as OpenGL and Vulkan.
- Autonomous Vehicle Simulations—Several of our customers are designing and simulating autonomous vehicles that include multiple real-time sensors. They can use ray tracing to simulate sensor input in real time.
The instances are compatible with a very long list of graphical and machine learning libraries on Linux, including NVENC, NVDEC, nvJPEG, OpenGL, Vulkan, CUDA, CuDNN, CuBLAS, and TensorRT.
The new G5g instances are available now, and you can start using them today in the US East (N. Virginia), US West (Oregon), and Asia-Pacific (Seoul, Singapore and Tokyo) Regions in On-Demand, Spot, Savings Plan, and Reserved Instance form. To learn more, see the EC2 pricing page.
G5g instances are available now in AWS Deep Learning AMIs with NVIDIA drivers and popular ML frameworks, Amazon Elastic Container Service (Amazon ECS), or Amazon Elastic Kubernetes Service (Amazon EKS) clusters for containerized ML applications.
You can send feedback to the AWS forum for Amazon EC2 or through your usual AWS Support contacts.