GPU-accelerated ECSs¶
GPU-accelerated ECSs provide outstanding floating-point computing capabilities. They are suitable for applications that require real-time, highly concurrent massive computing.
GPU-accelerated ECSs are classified as G series and P series of ECSs.
G series: Graphics-accelerated ECSs, which are suitable for 3D animation rendering and CAD
P series: Computing-accelerated or inference-accelerated ECSs, which are suitable for deep learning, scientific computing, and CAE
GPU-accelerated ECS Types¶
Recommended: Computing-accelerated P2s, Inference-accelerated Pi2, and GPU-accelerated Enhancement G6
Available now: All GPU models except the recommended ones. If available ECSs are sold out, use the recommended ones.
G series
P series
Computing-accelerated P2s (recommended)
Inference-accelerated Pi2 (recommended)
Helpful links:
Images Supported by GPU-accelerated ECSs¶
Type | Series | Supported Image |
|---|---|---|
Graphics-accelerated | G7 |
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Graphics-accelerated | G6 |
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Computing-accelerated | P3 |
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Computing-accelerated | P2s |
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Computing-accelerated | P2v |
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Inference-accelerated | Pi2 |
|
Graphics-accelerated Enhancement G7¶
Overview
G7 ECSs use NVIDIA A40 GPUs and support DirectX, Shader Model, OpenGL, and Vulkan. Each GPU provides 48 GiB of GPU memory. Theoretically, the peak FP32 is 37.4 TFLOPS and the peak TF32 tensor is 74.8 TFLOPS | 149.6 TFLOPS (sparsity enabled). They deliver two times the rendering performance and 1.4 times the graphics processing performance of RTX6000 GPUs to meet professional graphics processing requirements.
Select your desired GPU-accelerated ECS type and specifications.
Specifications
Flavor | vCPUs | Memory (GiB) | Max./Assured Bandwidth (Gbit/s) | Max. PPS (10,000) | Max. NIC Queues | Max. NICs | GPUs | GPU Memory (GiB) | Virtualization |
|---|---|---|---|---|---|---|---|---|---|
g7.12xlarge.8 | 48 | 384 | 35/18 | 750 | 16 | 8 | 1 x NVIDIA-A40 | 1 x 48 | KVM |
g7.24xlarge.8 | 96 | 768 | 40/36 | 850 | 16 | 8 | 2 x NVIDIA-A40 | 2 x 48 | KVM |
G7 ECS Features
CPU: 3rd Generation Intel® Xeon® Scalable 8378A processors (3.0 GHz of base frequency and 3.5 GHz of turbo frequency)
Graphics acceleration APIs
DirectX 12.07, Direct2D, DirectX Video Acceleration (DXVA)
Shader Model 5.17
OpenGL 4.68
Vulkan 1.18
CUDA, DirectCompute, OpenACC, and OpenCL
A single card is equipped with 10,752 CUDA cores, 84 second-generation RT cores, and 336 third-generation Tensor cores.
Graphics applications accelerated
Heavy-load CPU inference
Application flow identical to common ECSs
Automatic scheduling of G7 ECSs to AZs where NVIDIA A40 GPUs are used
One NVENC (encoding) engine and two NVDEC (decoding) engines (including AV1 decoding) embedded
Supported Common Software
G7 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 G7 ECSs. G7 ECSs support the following commonly used graphics processing software:
AutoCAD
3DS MAX
MAYA
Agisoft PhotoScan
ContextCapture
Adobe Premiere Pro
Solidworks
Unreal Engine
Blender
Vray
Notes
After a G7 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.
Note
Resources will be released after a G7 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.
G7 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.
If a G7 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.
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 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
Flavor | vCPUs | Memory (GiB) | Max./Assured Bandwidth (Gbit/s) | Max. PPS (10,000) | Max. NIC Queues | Max. NICs | GPUs | GPU Memory (GiB) | Virtualization |
|---|---|---|---|---|---|---|---|---|---|
g6.4xlarge.4 | 16 | 64 | 25/15 | 200 | 8 | 8 | 1 x T4 | 16 | KVM |
g6.10xlarge.7 | 40 | 280 | 25/15 | 200 | 16 | 8 | 1 x T4 | 16 | KVM |
g6.20xlarge.7 | 80 | 560 | 30/30 | 400 | 32 | 16 | 2 x T4 | 32 | KVM |
Note
A G6.10xlarge.7 ECS exclusively uses a T4 GPU for professional graphics acceleration. Such an ECS can be used for heavy-load CPU inference.
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.
Note
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.
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.
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.
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.
Computing-accelerated P3¶
Overview
P3 ECSs use NVIDIA A100 GPUs and provide flexibility and ultra-high-performance computing. P3 ECSs have strengths in AI-based deep learning, scientific computing, Computational Fluid Dynamics (CFD), computing finance, seismic analysis, molecular modeling, and genomics. Theoretically, the FP32 is 19.5 TFLOPS and the TF32 tensor core is 156 TFLOPS | 312 TFLOPS (sparsity enabled).
Specifications
Flavor | vCPUs | Memory (GiB) | Max./Assured Bandwidth (Gbit/s) | Max. PPS (10,000) | Max. NIC Queues | Max. NICs | GPUs | GPU Memory (GiB) | Virtualization |
|---|---|---|---|---|---|---|---|---|---|
p3.2xlarge.8 | 8 | 64 | 10/4 | 100 | 4 | 4 | 1 x NVIDIA A100 80GB | 80 | KVM |
p3.4xlarge.8 | 16 | 128 | 15/8 | 200 | 8 | 8 | 2 x NVIDIA A100 80GB | 160 | KVM |
p3.8xlarge.8 | 32 | 256 | 25/15 | 350 | 16 | 8 | 4 x NVIDIA A100 80GB | 320 | KVM |
p3.16xlarge.8 | 64 | 512 | 36/30 | 700 | 32 | 8 | 8 x NVIDIA A100 80GB | 640 | KVM |
P3 ECS Features
CPU: 2nd Generation Intel® Xeon® Scalable 6248R processors and 3.0 GHz of base frequency
Up to eight NVIDIA A100 GPUs on an ECS
NVIDIA CUDA parallel computing and common deep learning frameworks, such as TensorFlow, Caffe, PyTorch, and MXNet
19.5 TFLOPS of single-precision computing and 9.7 TFLOPS of double-precision computing on a single GPU
NVIDIA Tensor cores with 156 TFLOPS of single- and double-precision computing for deep learning
Up to 40 Gbit/s of network bandwidth on a single ECS
80 GB HBM2 GPU memory per graphics card, with a bandwidth of 1,935 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, P3 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 P3 ECSs.
Supported Common Software
P3 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 P3 ECSs. P3 ECSs support the following commonly used software:
Common deep learning frameworks, such as TensorFlow, Spark, PyTorch, MXNet, and Caffee
CUDA GPU rendering supported by RedShift for Autodesk 3dsMax and V-Ray for 3ds Max
Agisoft PhotoScan
MapD
More than 2,000 GPU-accelerated applications such as Amber, NAMD, and VASP
Notes
After a P3 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.
Note
Resources will be released after a P3 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.
If a P3 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.
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
Flavor | vCPUs | Memory (GiB) | Max./Assured Bandwidth (Gbit/s) | Max. PPS (10,000) | Max. NIC Queues | Max. NICs | GPUs | GPU Connection | GPU Memory (GiB) | Virtualization | Hardware |
|---|---|---|---|---|---|---|---|---|---|---|---|
p2s.2xlarge.8 | 8 | 64 | 10/4 | 50 | 4 | 4 | 1 x V100 | PCIe Gen3 | 1 x 32 GiB | KVM | CPU: 2nd Generation Intel® Xeon® Scalable Processor 6278 |
p2s.4xlarge.8 | 16 | 128 | 15/8 | 100 | 8 | 8 | 2 x V100 | PCIe Gen3 | 2 x 32 GiB | KVM | |
p2s.8xlarge.8 | 32 | 256 | 25/15 | 200 | 16 | 8 | 4 x V100 | PCIe Gen3 | 4 x 32 GiB | KVM | |
p2s.16xlarge.8 | 64 | 512 | 30/30 | 400 | 32 | 8 | 8 x V100 | PCIe Gen3 | 8 x 32 GiB | KVM |
P2s 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 eight NVIDIA Tesla V100 GPUs on an ECS
NVIDIA CUDA parallel computing and common deep learning frameworks, such as TensorFlow, Caffe, PyTorch, and MXNet
14 TFLOPS of single-precision computing and 7 TFLOPS of double-precision computing
NVIDIA Tensor cores with 112 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, P2s 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 P2s ECSs.
Supported Common Software
P2s 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 P2s ECSs. P2s ECSs support the following commonly used software:
Common deep learning frameworks, such as TensorFlow, Caffe, PyTorch, and MXNet
CUDA GPU rendering supported by RedShift for Autodesk 3dsMax and V-Ray for 3ds Max
Agisoft PhotoScan
MapD
Notes
After a P2s 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.
Note
Resources will be released after a P2s 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.
By default, P2s ECSs created using a Windows public image have the Tesla driver installed.
If a P2s 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.
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
Flavor | vCPUs | Memory (GiB) | Max./Assured Bandwidth (Gbit/s) | Max. PPS (10,000) | Max. NIC Queues | Max. NICs | GPUs | GPU Connection | GPU Memory (GiB) | Virtualization | Hardware |
|---|---|---|---|---|---|---|---|---|---|---|---|
p2v.2xlarge.8 | 8 | 64 | 10/4 | 50 | 4 | 4 | 1 x V100 | N/A | 1 x 16 GiB | KVM | CPU: Intel® Xeon® Skylake-SP Gold 6151 v5 |
p2v.4xlarge.8 | 16 | 128 | 15/8 | 100 | 8 | 8 | 2 x V100 | NVLink | 2 x 16 GiB | KVM | |
p2v.8xlarge.8 | 32 | 256 | 25/15 | 200 | 16 | 8 | 4 x V100 | NVLink | 4 x 16 GiB | KVM | |
p2v.16xlarge.8 | 64 | 512 | 30/30 | 400 | 32 | 8 | 8 x V100 | NVLink | 8 x 16 GiB | KVM |
P2v 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
16 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, P2v 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 P2v ECSs.
Supported Common Software
P2v 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 P2v ECSs. P2v ECSs support the following commonly used software:
Common deep learning frameworks, such as TensorFlow, Caffe, PyTorch, and MXNet
CUDA GPU rendering supported by RedShift for Autodesk 3dsMax and V-Ray for 3ds Max
Agisoft PhotoScan
MapD
Notes
After a P2v 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.
Note
Resources will be released after a P2v 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.
By default, P2v ECSs created using a Windows public image have the Tesla driver installed.
By default, P2v ECSs created using a Linux public image do not have a Tesla driver installed. After the ECS is created, install a driver on it for computing acceleration. For details, see Installing a Tesla Driver and CUDA Toolkit on a GPU-accelerated ECS.
If a P2v 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.
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
Flavor | vCPUs | Memory (GiB) | Max./Assured Bandwidth (Gbit/s) | Max. PPS (10,000) | Max. NIC Queues | Max. NICs | GPUs | GPU Memory (GiB) | Local Disks | Virtualization | Hardware |
|---|---|---|---|---|---|---|---|---|---|---|---|
pi2.2xlarge.4 | 8 | 32 | 10/4 | 50 | 4 | 4 | 1 x T4 | 1 x 16 GiB | N/A | KVM | CPU: Intel® Xeon® Skylake 6151 3.0 GHz or Intel® Xeon® Cascade Lake 6278 2.6 GHz |
pi2.3xlarge.4 | 12 | 48 | 12/6 | 80 | 6 | 6 | 1 x T4 | 1 x 16 GiB | N/A | KVM | |
pi2.4xlarge.4 | 16 | 64 | 15/8 | 100 | 8 | 8 | 2 x T4 | 2 x 16 GiB | N/A | KVM | |
pi2.8xlarge.4 | 32 | 128 | 25/15 | 200 | 16 | 8 | 4 x T4 | 4 x 16 GiB | N/A | KVM | |
pi2.16xlarge.4 | 64 | 256 | 30/30 | 400 | 32 | 8 | 8 x T4 | 8 x 16 GiB | N/A | KVM |
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.
Note
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.
Pi2 ECSs support automatic recovery when the hosts accommodating such ECSs become faulty.
By default, Pi2 ECSs created using a Windows public image have the Tesla driver installed.
By default, Pi2 ECSs created using a Linux public image do not have a Tesla driver installed. After the ECS is created, install a driver on it for computing acceleration. For details, see Installing a Tesla Driver and CUDA Toolkit on a GPU-accelerated ECS.
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.