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High performance GPU-based instances for graphics-intensive applications and machine learning inference

Amazon EC2 G5 instances are the latest generation of NVIDIA GPU-based instances that can be used for a wide range of graphics-intensive and machine learning use cases. They deliver up to 3x better performance for graphics-intensive applications and machine learning inference and up to 3.3x higher performance for machine learning training compared to Amazon EC2 G4dn instances.

Customers can use G5 instances for graphics-intensive applications such as remote workstations, video rendering, and gaming to produce high fidelity graphics in real time. With G5 instances, machine learning customers get high performance and cost-efficient infrastructure to train and deploy larger and more sophisticated models for natural language processing, computer vision, and recommender engine use cases.

G5 instances feature up to 8 mg电子游戏试玩官方入口s and second generation AMD EPYC processors. They also support up to 192 vCPUs, up to 100 Gbps of network bandwidth, and up to 7.6 TB of local NVMe SSD storage.

Benefits

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G5 instances deliver up to 3x higher graphics performance and up to 40% better price performance than G4dn instances. They have more ray tracing cores than any other GPU-based EC2 instance, feature 24 GB of memory per GPU, and support NVIDIA RTX technology. This makes them ideal for rendering realistic scenes faster, running powerful virtual workstations, and supporting graphics heavy applications at higher fidelity.

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G5 instances deliver up to 3x higher performance and up to 40% better price performance for machine learning inference compared to G4dn instances. They are a highly performant and cost-efficient solution for customers who want to use NVIDIA libraries such as TensorRT, CUDA, and cuDNN to run their ML applications.

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G5 instances offer up to 15% lower cost-to-train than Amazon EC2 P3 instances. They also deliver up to 3.3x higher performance for ML training compared to G4dn instances. This makes them a cost-efficient solution for training moderately complex and single node machine learning models for natural language processing, computer vision, and recommender engine use cases.

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G5 instances are built on the Amazon Nitro System, a combination of dedicated hardware and lightweight hypervisor which delivers practically all of the compute and memory resources of the host hardware to your instances for better overall performance and security. With G5 instances, the Nitro system provisions the GPUs in a pass-through mode, providing performance comparable to bare-metal.

Features

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G5 instances are the first in the cloud to feature mg电子游戏试玩官方入口s that deliver high performance for graphics-intensive and machine learning applications. Each instance features up to 8 A10G Tensor Core GPUs that come with 80 ray tracing cores and 24 GB of memory per GPU. They also offer 320 third-generation NVIDIA Tensor Cores delivering up to 250 TOPS resulting in high performance for ML workloads.

NVIDIA drivers

G5 instances offer NVIDIA RTX Enterprise and gaming drivers to customers at no additional cost. NVIDIA RTX Enterprise drivers can be used to provide high quality virtual workstations for a wide range of graphics-intensive workloads. NVIDIA gaming drivers provide unparalleled graphics and compute support for game development. G5 instances also support CUDA, cuDNN, NVENC, TensorRT, cuBLAS, OpenCL, DirectX 11/12, Vulkan 1.1, and OpenGL 4.5 libraries.

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G5 instances come with up to 100 Gbps of networking throughput enabling them to support the low latency needs of machine learning inference and graphics-intensive applications. 24 GB of memory per GPU along with support for up to 7.6 TB of local NVMe SSD storage enable local storage of large models and datasets for high performance machine learning training and inference. G5 instances can also store large video files locally resulting in increased graphics performance and the ability to render larger and more complex video files.

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G5 instances are built on the Amazon Nitro System, which is a rich collection of building blocks that offloads many of the traditional virtualization functions to dedicated hardware and software to deliver high performance, high availability, and high security while also reducing virtualization overhead.

Product details

Instance Size GPU GPU Memory (GiB) vCPUs Memory (GiB) Storage (GB) Network Bandwidth (Gbps) EBS Bandwidth (Gbps)
Single GPU VMs g5.xlarge 1 24 4 16 1x250 Up to 10 Up to 3.5
g5.2xlarge 1 24 8 32 1x450 Up to 10 Up to 3.5
g5.4xlarge 1 24 16 64 1x600 Up to 25 8
g5.8xlarge 1 24 32 128 1x900 25 16
g5.16xlarge 1 24 64 256 1x1900 25 16
Multi GPU VMs g5.12xlarge 4 96 48 192 1x3800 40 16
g5.24xlarge 4 96 96 384 1x3800 50 19
g5.48xlarge 8 192 192 768 2x3800 100 19

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