GPU-accelerated ECSs

GPU-accelerated ECSs

Recommended: Computing-accelerated P2s and Inference-accelerated Pi2

Available now: All GPU models except the recommended ones. If available ECSs are sold out, use the recommended ones.

• G series
  • GPU-accelerated Enhancement G6v
  • GPU-accelerated Enhancement G6
  • Graphics-accelerated Enhancement G5
  • Graphics-accelerated Enhancement G3
  • Graphics-accelerated G1
• P series
  • Computing-accelerated P2vs
  • Computing-accelerated P2s (recommended)
  • Computing-accelerated P2v
  • Inference-accelerated Pi2 (recommended)
  • Inference-accelerated Pi1

Images Supported by GPU-accelerated ECSs

GPU-accelerated Enhancement G6v

Overview

G6v ECSs use NVIDIA Tesla T4 GPUs to support DirectX, OpenGL, and Vulkan. Each GPU provides 16 GiB of GPU memory. The theoretical Pixel rate is 101.8 Gpixel/s and Texture rate is 254.4 GTexel/s, meeting professional graphics processing requirements. Each T4 GPU can be virtualized to be shared by two, four, or eight ECSs.

Select your desired GPU-accelerated ECS type and specifications.

Specifications

G6v ECS Features

• CPU: 2nd Generation Intel® Xeon® Scalable 6266 processors (3.0 GHz of base frequency and 3.4 GHz of turbo frequency)
• Graphics acceleration APIs
  • DirectX 12, Direct2D, and DirectX Video Acceleration (DXVA)
  • OpenGL 4.5
  • Vulkan 1.0
• CUDA and OpenCL
• NVIDIA T4 GPUs with 16 GB GPU memory
  Virtual shards of instances:

  • 1/8, 1/4, and 1/2 of computing performance of NVIDIA Tesla T4
  • 2 GB, 4 GB, and 8 GB GPU memory
• Graphics applications accelerated
• Heavy-load CPU inference
• Automatic scheduling of G6v ECSs to AZs where NVIDIA T4 GPUs are used
• One NVENC engine and two NVDEC engines embedded

Supported Common Software

G6v ECSs are used in graphics acceleration scenarios, such as image rendering, cloud desktop, cloud gaming, and 3D visualization. If the software relies on GPU DirectX and OpenGL hardware acceleration, use G6v ECSs. G6v ECSs support the following commonly used graphics processing software:

• AutoCAD
• 3ds Max
• MAYA
• Agisoft PhotoScan
• ContextCapture

Notes

• After a G6v ECS is stopped, basic resources (including vCPUs, memory, image, and GPUs) are not billed, but its system disk is billed based on the disk capacity. If other products, such as EVS disks, EIP, and bandwidth are associated with the ECS, these products are billed separately.
  

  Resources will be released after a G6v ECS is stopped. If resources are insufficient at the next start, the start may fail. If you want to use such an ECS for a long period of time, do not stop the ECS or change its billing mode to yearly/monthly.

• G6v ECSs created using a public image have had the GRID driver of a specific version installed by default. However, you need to purchase and configure a GRID license by yourself. Ensure that the GRID driver version meets service requirements.

  For details about how to configure a GRID license, see Installing a GRID Driver on a GPU-accelerated ECS.

• If a G6v ECS is created using a private image, make sure that the GRID driver was installed during the private image creation. If the GRID driver has not been installed, install the driver for graphics acceleration after the ECS is created.

  For details, see Installing a GRID Driver on a GPU-accelerated ECS.

• GPU-accelerated ECSs differ greatly in general-purpose and heterogeneous computing power. Their specifications can only be changed to other specifications of the same instance type.
• GPU-accelerated ECSs do not support live migration.

GPU-accelerated Enhancement G6

Overview

G6 ECSs use NVIDIA Tesla T4 GPUs to support DirectX, OpenGL, and Vulkan and provide 16 GiB of GPU memory. The theoretical Pixel rate is 101.8 Gpixel/s and Texture rate 254.4 GTexel/s, meeting professional graphics processing requirements.

Select your desired GPU-accelerated ECS type and specifications.

Specifications

G6 ECS Features

• CPU: 2nd Generation Intel® Xeon® Scalable 6266 processors (3.0 GHz of base frequency and 3.4 GHz of turbo frequency)
• Graphics acceleration APIs
  • DirectX 12, Direct2D, and DirectX Video Acceleration (DXVA)
  • OpenGL 4.5
  • Vulkan 1.0
• CUDA and OpenCL
• NVIDIA T4 GPUs
• Graphics applications accelerated
• Heavy-load CPU inference
• Automatic scheduling of G6 ECSs to AZs where NVIDIA T4 GPUs are used
• One NVENC engine and two NVDEC engines embedded

Supported Common Software

G6 ECSs are used in graphics acceleration scenarios, such as video rendering, cloud desktop, and 3D visualization. If the software relies on GPU DirectX and OpenGL hardware acceleration, use G6 ECSs. G6 ECSs support the following commonly used graphics processing software:

• AutoCAD
• 3ds Max
• MAYA
• Agisoft PhotoScan
• ContextCapture

Notes

• After a G6 ECS is stopped, basic resources (including vCPUs, memory, image, and GPUs) are not billed, but its system disk is billed based on the disk capacity. If other products, such as EVS disks, EIP, and bandwidth are associated with the ECS, these products are billed separately.
  

  Resources will be released after a G6 ECS is stopped. If resources are insufficient at the next start, the start may fail. If you want to use such an ECS for a long period of time, do not stop the ECS or change its billing mode to yearly/monthly.

• G6 ECSs created using a public image have had the GRID driver of a specific version installed by default. However, you need to purchase and configure a GRID license by yourself. Ensure that the GRID driver version meets service requirements.

  For details about how to configure a GRID license, see Installing a GRID Driver on a GPU-accelerated ECS.

• If a G6 ECS is created using a private image, make sure that the GRID driver was installed during the private image creation. If not, install the driver for graphics acceleration after the ECS is created.

  For details, see Installing a GRID Driver on a GPU-accelerated ECS.

• GPU-accelerated ECSs differ greatly in general-purpose and heterogeneous computing power. Their specifications can only be changed to other specifications of the same instance type.
• GPU-accelerated ECSs do not support live migration.

GPU-accelerated Enhancement G5

Overview

G5 ECSs use NVIDIA Tesla V100 GPUs and support DirectX, OpenGL, and Vulkan. These ECSs provide 16 GiB of GPU memory and up to 4096 x 2160 resolution, meeting requirements on professional graphics processing.

Select your desired GPU-accelerated ECS type and specifications.

Specifications

A g5.8xlarge.4 ECS exclusively uses a V100 GPU for professional graphics acceleration. Such an ECS can be used for heavy-load CPU inference.

Supported Common Software

Notes

• After a G5 ECS is stopped, basic resources (including vCPUs, memory, image, and GPUs) are not billed, but its system disk is billed based on the disk capacity. If other products, such as EVS disks, EIP, and bandwidth are associated with the ECS, these products are billed separately.
  

  Resources will be released after a G5 ECS is stopped. If resources are insufficient at the next start, the start may fail. If you want to use such an ECS for a long period of time, do not stop the ECS or change its billing mode to yearly/monthly.

• For G5 ECSs, you need to configure the GRID license after the ECS is created.
• G5 ECSs created using a public image have had the GRID driver of a specific version installed by default. However, you need to purchase and configure a GRID license by yourself. Ensure that the GRID driver version meets service requirements.

  For details about how to configure a GRID license, see Installing a GRID Driver on a GPU-accelerated ECS.

• If a G5 ECS is created using a private image, make sure that the GRID driver was installed during the private image creation. If not, install the driver for graphics acceleration after the ECS is created.

  For details, see Installing a GRID Driver on a GPU-accelerated ECS.

• GPU-accelerated ECSs differ greatly in general-purpose and heterogeneous computing power. Their specifications can only be changed to other specifications of the same instance type.
• GPU-accelerated ECSs do not support live migration.

GPU-accelerated Enhancement G3

Overview

G3 ECSs are based on PCI passthrough and exclusively use GPUs for professional graphics acceleration. In addition, G3 ECSs use NVIDIA Tesla M60 GPUs and support DirectX and OpenGL with up to 16 GiB of GPU memory and 4096 x 2160 resolution. They are ideal for professional graphics workstations.

Specifications

Table 6 G3 ECS specifications

Flavor	vCPUs	Memory (GiB)	Max./Assured Bandwidth (Gbit/s)	Max. PPS (10,000)	Max. NIC Queues	GPUs	GPU Memory (GiB)	Virtualization
g3.4xlarge.4	16	64	8/2.5	50	2	1 x M60	1 x 8	KVM
g3.8xlarge.4	32	128	10/5	100	4	2 x M60	2 x 8	KVM

Every NVIDIA Tesla M60 card is equipped with two M60 GPUs, each of which provides 2,048 CUDA cores and 8 GiB of GPU memory. M60 in G series of ECSs indicates M60 GPUs, but not M60 cards.

Notes

• After a G3 ECS is stopped, basic resources (including vCPUs, memory, image, and GPUs) are not billed, but its system disk is billed based on the disk capacity. If other products, such as EVS disks, EIP, and bandwidth are associated with the ECS, these products are billed separately.
  

  Resources will be released after a G3 ECS is stopped. If resources are insufficient at the next start, the start may fail. If you want to use such an ECS for a long period of time, do not stop the ECS or change its billing mode to yearly/monthly.

• When the Windows OS running on a G3 ECS is started, the GRID driver is loaded by default, and vGPUs are used for video output by default. In such a case, the remote login function provided on the management console is not supported. To access such an ECS, use RDP, such as Windows MSTSC. Then, install a third-party VDI tool on the ECS for remote login, such as VNC.
• By default, G3 ECSs created using a public image have had the GRID driver of a specific version installed.
• If a G3 ECS is created using a private image, make sure that the GRID driver was installed during the private image creation. If not, install the driver for graphics acceleration after the ECS is created. For details, see Installing a GRID Driver on a GPU-accelerated ECS.
• GPU-accelerated ECSs differ greatly in general-purpose and heterogeneous computing power. Their specifications can only be changed to other specifications of the same instance type.
• GPU-accelerated ECSs do not support live migration.

GPU-accelerated G1

Overview

G1 ECSs are based on NVIDIA GRID vGPUs and provide economical graphics acceleration. They use NVIDIA Tesla M60 GPUs and support DirectX and OpenGL. The ECSs have up to 8 GiB of GPU memory and 4096 x 2160 resolution, and are used for applications that require high performance in graphics rendering.

Specifications

M60-xQ support vGPUs. x can be 1, 2, 4, or 8, indicating that M60 GPUs are virtualized to vGPUs with different specifications and models using GRID. x specifies the vGPU memory, and Q indicates that the vGPU of this type is designed to work in workstations and desktop scenarios. For more details about GRID vGPUs, see GRID VIRTUAL GPU User Guide.

Computing-accelerated P2vs

Overview

P2vs ECSs use NVIDIA Tesla V100 GPUs (32 GB GPU memory) to provide flexibility, high-performance computing, and cost-effectiveness. These ECSs use GPU NVLink for direct communication between GPUs, improving data transmission efficiency. P2vs ECSs provide outstanding general computing capabilities and have strengths in AI-based deep learning, scientific computing, Computational Fluid Dynamics (CFD), computing finance, seismic analysis, molecular modeling, and genomics.

Specifications

P2vs ECS Features

• CPU: Intel® Xeon® Scalable 6151 processors (3.0 GHz of base frequency and 3.4 GHz of turbo frequency).
• Up to eight NVIDIA Tesla V100 GPUs on an ECS
• NVIDIA CUDA parallel computing and common deep learning frameworks, such as TensorFlow, Caffe, PyTorch, and MXNet
• 15.7 TFLOPS of single-precision computing and 7.8 TFLOPS of double-precision computing
• NVIDIA Tensor cores with 125 TFLOPS of single- and double-precision computing for deep learning
• Up to 30 Gbit/s of network bandwidth on a single ECS
• 32 GiB of HBM2 GPU memory with a bandwidth of 900 Gbit/s
• Comprehensive basic capabilities
  • User-defined network with flexible subnet division and network access policy configuration
  • Mass storage, elastic expansion, and backup and restoration
  • Elastic scaling
• Flexibility

  Similar to other types of ECSs, P2vs ECSs can be provisioned in a few minutes.

• Excellent supercomputing ecosystem

  The supercomputing ecosystem allows you to build up a flexible, high-performance, cost-effective computing platform. A large number of HPC applications and deep-learning frameworks can run on P2vs ECSs.

Supported Common Software

P2vs ECSs are used in computing acceleration scenarios, such as deep learning training, inference, scientific computing, molecular modeling, and seismic analysis. If the software is required to support GPU CUDA, use P2vs ECSs.

Notes

• After a P2vs ECS is stopped, basic resources (including vCPUs, memory, image, and GPUs) are not billed, but its system disk is billed based on the disk capacity. If other products, such as EVS disks, EIP, and bandwidth are associated with the ECS, these products are billed separately.
  

  Resources will be released after a P2vs ECS is stopped. If resources are insufficient at the next start, the start may fail. If you want to use such an ECS for a long period of time, do not stop the ECS or change its billing mode to yearly/monthly.

• By default, P2vs ECSs created using a public image have the Tesla driver installed.
• If a P2vs ECS is created using a private image, make sure that the Tesla driver was installed during the private image creation. If not, install the driver for computing acceleration after the ECS is created. For details, see Installing a Tesla Driver and CUDA Toolkit on a GPU-accelerated ECS.
• GPU-accelerated ECSs differ greatly in general-purpose and heterogeneous computing power. Their specifications can only be changed to other specifications of the same instance type.
• GPU-accelerated ECSs do not support live migration.

Computing-accelerated P2s

Overview

P2s ECSs use NVIDIA Tesla V100 GPUs to provide flexibility, high-performance computing, and cost-effectiveness. P2s ECSs provide outstanding general computing capabilities and have strengths in AI-based deep learning, scientific computing, Computational Fluid Dynamics (CFD), computing finance, seismic analysis, molecular modeling, and genomics.

Specifications

Supported Common Software

Computing-accelerated P2v

Overview

P2v ECSs use NVIDIA Tesla V100 GPUs and deliver high flexibility, high-performance computing, and high cost-effectiveness. These ECSs use GPU NVLink for direct communication between GPUs, improving data transmission efficiency. P2v ECSs provide outstanding general computing capabilities and have strengths in AI-based deep learning, scientific computing, Computational Fluid Dynamics (CFD), computing finance, seismic analysis, molecular modeling, and genomics.

Specifications

Supported Common Software

Inference-accelerated Pi2

Overview

Pi2 ECSs use NVIDIA Tesla T4 GPUs dedicated for real-time AI inference. These ECSs use the T4 INT8 calculator for up to 130 TOPS of INT8 computing. The Pi2 ECSs can also be used for light-load training.

Specifications

Pi2 ECS Features

• CPU: 2nd Generation Intel® Xeon® Scalable 6278 processors (2.6 GHz of base frequency and 3.5 GHz of turbo frequency), or Intel® Xeon® Scalable 6151 processors (3.0 GHz of base frequency and 3.4 GHz of turbo frequency)
• Up to four NVIDIA Tesla T4 GPUs on an ECS
• GPU hardware passthrough
• Up to 8.1 TFLOPS of single-precision computing on a single GPU
• Up to 130 TOPS of INT8 computing on a single GPU
• 16 GiB of GDDR6 GPU memory with a bandwidth of 320 GiB/s on a single GPU
• One NVENC engine and two NVDEC engines embedded

Supported Common Software

Pi2 ECSs are used in GPU-based inference computing scenarios, such as image recognition, speech recognition, and natural language processing. The Pi2 ECSs can also be used for light-load training.

Pi2 ECSs support the following commonly used software:

• Deep learning frameworks, such as TensorFlow, Caffe, PyTorch, and MXNet

Notes

• After a Pi2 ECS is stopped, basic resources (including vCPUs, memory, image, and GPUs) are not billed, but its system disk is billed based on the disk capacity. If other products, such as EVS disks, EIP, and bandwidth are associated with the ECS, these products are billed separately.
  

  Resources will be released after a Pi2 ECS is stopped. If resources are insufficient at the next start, the start may fail. If you want to use such an ECS for a long period of time, do not stop the ECS or change its billing mode to yearly/monthly.

• Pi2 ECSs support automatic recovery when the hosts accommodating such ECSs become faulty.
• By default, Pi2 ECSs created using a public image have the Tesla driver installed.
• If a Pi2 ECS is created using a private image, make sure that the Tesla driver was installed during the private image creation. If not, install the driver for computing acceleration after the ECS is created. For details, see Installing a Tesla Driver and CUDA Toolkit on a GPU-accelerated ECS.
• GPU-accelerated ECSs differ greatly in general-purpose and heterogeneous computing power. Their specifications can only be changed to other specifications of the same instance type.
• GPU-accelerated ECSs do not support live migration.

Inference-accelerated Pi1

Overview

Pi1 ECSs use NVIDIA Tesla P4 GPUs dedicated for real-time AI inference. Working with P4 INT8 calculators, Pi1 ECSs have shortened the inference latency by 15 times. Working with hardware decoding engines, Pi1 ECSs concurrently support real-time 35-channel HD video transcoding and inference.

Specifications

Supported Common Software

Pi1 ECSs are used in GPU-based inference computing scenarios, such as image recognition, speech recognition, and natural language processing.

Pi1 ECSs support the following commonly used software:

• Deep learning frameworks, such as TensorFlow, Caffe, PyTorch, and MXNet

Notes

• After a pay-per-use Pi1 ECS is stopped, basic resources (including vCPUs, memory, image, and GPUs) are not billed, but its system disk is billed based on the disk capacity. If other products, such as EVS disks, EIP, and bandwidth are associated with the ECS, these products are billed separately.
  

  Resources will be released after a pay-per-use Pi1 ECS is stopped. If resources are insufficient at the next start, the start may fail. If you want to use such an ECS for a long period of time, do not stop the ECS or change its billing mode to yearly/monthly.

• Pi1 ECSs do not support specifications change.
• Pi1 ECSs support automatic recovery when the hosts accommodating such ECSs become faulty.
• By default, Pi1 ECSs created using a public image have the Tesla driver installed.
• If a Pi1 ECS is created using a private image, make sure that the Tesla driver was installed during the private image creation. If not, install the driver for computing acceleration after the ECS is created. For details, see Installing a Tesla Driver and CUDA Toolkit on a GPU-accelerated ECS.
• GPU-accelerated ECSs differ greatly in general-purpose and heterogeneous computing power. Their specifications can only be changed to other specifications of the same instance type.
• GPU-accelerated ECSs do not support live migration.