Amazon announced the general availability of Amazon EC2 G5g instances that extend Graviton2 price-performance benefits to GPU-based workloads featuring NVIDIA T4G Tensor Core GPUs. The AWS G5g instances provide the best price-performance for Android game streaming, with up to 25 Gbps of networking bandwidth and 19 Gbps of EBS bandwidth, while providing 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.
Amazon announced the general availability of Amazon EC2 G5g instances that extend Graviton2 price-performance benefits to GPU-based workloads featuring NVIDIA T4G Tensor Core GPUs. The AWS G5g instances provide the best price-performance for Android game streaming, with up to 25 Gbps of networking bandwidth and 19 Gbps of EBS bandwidth, while providing 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.
Amazon EC2 G5g Sizing
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 |
| g5g.8xlarge | 32 | 64 | 1 | 16 | 9 | 12 |
| g5g.16xlarge | 64 | 128 | 2 | 32 | 19 | 25 |
| g5g.metal | 64 | 128 | 2 | 32 | 19 | 25 |
These instances are a great fit for many workloads such as:
- 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.
- 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 AWS 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.
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