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.
Hyper-threading is enabled for this type of ECSs by default. Each vCPU is a thread of a CPU core.
GPU-accelerated ECS Types
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
- P series
- Computing-accelerated P2vs
- Computing-accelerated P2s (recommended)
- Computing-accelerated P2v
- Inference-accelerated Pi2 (recommended)
- Inference-accelerated Pi1
Type |
Series |
GPU |
CUDA Cores per GPU |
Single-GPU Performance |
Application |
Remarks |
---|---|---|---|---|---|---|
Graphics-accelerated |
G6v |
NVIDIA T4 (vGPU virtualization) |
2,560 |
|
Cloud desktop, image rendering, 3D visualization, and heavy-load graphics design |
Hyper-threading (Enabling or Disabling Hyper-Threading) |
Graphics-accelerated |
G6 |
NVIDIA T4 (GPU passthrough) |
2,560 |
|
Cloud desktop, image rendering, 3D visualization, and heavy-load graphics design |
Hyper-threading (Enabling or Disabling Hyper-Threading) |
Graphics-accelerated |
G5 |
NVIDIA V100 |
5,120 |
|
Cloud desktop, image rendering, 3D visualization, and heavy-load graphics design |
Hyper-threading (Enabling or Disabling Hyper-Threading) |
Graphics-accelerated |
G3 |
NVIDIA M60 (GPU passthrough) |
2,048 |
4.8 TFLOPS of single-precision floating-point computing |
Cloud desktop, image rendering, 3D visualization, and heavy-load graphics design |
- |
Graphics-accelerated |
G1 |
NVIDIA M60 (GPU virtualization) |
2,048 |
4.8 TFLOPS of single-precision floating-point computing |
Cloud desktop, image rendering, 3D visualization, and heavy-load graphics design |
- |
Computing-accelerated |
P2vs |
NVIDIA V100 NVLink (GPU passthrough) |
5,120 |
|
Machine learning, deep learning, inference training, scientific computing, seismic analysis, computing finance, rendering, multimedia encoding and decoding |
Hyper-threading (Enabling or Disabling Hyper-Threading) |
Computing-accelerated |
P2s |
NVIDIA V100 |
5,120 |
|
AI deep learning training, scientific computing, computational fluid dynamics, computational finance, seismic analysis, molecular modeling, and genomics. |
Hyper-threading (Enabling or Disabling Hyper-Threading) |
Computing-accelerated |
P2v |
NVIDIA V100 NVLink (GPU passthrough) |
5,120 |
|
Machine learning, deep learning, inference training, scientific computing, seismic analysis, computing finance, rendering, multimedia encoding and decoding |
Hyper-threading (Enabling or Disabling Hyper-Threading) |
Inference-accelerated |
Pi2 |
NVIDIA T4 (GPU passthrough) |
2,560 |
|
Machine learning, deep learning, inference training, scientific computing, seismic analysis, computing finance, rendering, multimedia encoding and decoding |
Hyper-threading (Enabling or Disabling Hyper-Threading) |
Inference-accelerated |
Pi1 |
NVIDIA P4 (GPU passthrough) |
2,560 |
5.5 TFLOPS of single-precision floating-point computing |
Machine learning, deep learning, inference training, scientific computing, seismic analysis, computing finance, rendering, multimedia encoding and decoding |
Hyper-threading (Enabling or Disabling Hyper-Threading) |
Images Supported by GPU-accelerated ECSs
Type |
Series |
Supported Image |
---|---|---|
Graphics-accelerated |
G6v |
|
Graphics-accelerated |
G6 |
|
Graphics-accelerated |
G5 |
|
Graphics-accelerated |
G3 |
|
Graphics-accelerated |
G1 |
|
Computing-accelerated |
P2vs |
|
Computing-accelerated |
P2s |
|
Computing-accelerated |
P2v |
|
Inference-accelerated |
Pi2 |
|
Inference-accelerated |
Pi1 |
|
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
Flavor |
vCPUs |
Memory (GiB) |
Max./Assured Bandwidth (Gbit/s) |
Max. PPS (10,000) |
Max. NIC Queues |
GPUs |
GPU Memory (GiB) |
Virtualization |
---|---|---|---|---|---|---|---|---|
g6v.2xlarge.2 |
8 |
16 |
6/2 |
35 |
4 |
1/8 × T4 |
2 |
KVM |
g6v.2xlarge.4 |
8 |
32 |
10/4 |
50 |
4 |
1/4 × T4 |
4 |
KVM |
g6v.4xlarge.4 |
16 |
64 |
15/8 |
100 |
8 |
1/2 × T4 |
8 |
KVM |
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
- 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
Flavor |
vCPUs |
Memory (GiB) |
Max./Assured Bandwidth (Gbit/s) |
Max. PPS (10,000) |
Max. NIC Queues |
Max. NICs |
GPUs |
GPU Memory (GiB) |
Virtualization |
---|---|---|---|---|---|---|---|---|---|
g6.xlarge.4 |
4 |
16 |
6/2 |
200 |
8 |
8 |
1 × T4 |
16 |
KVM |
g6.4xlarge.4 |
16 |
64 |
15/8 |
200 |
8 |
8 |
1 × T4 |
16 |
KVM |
g6.6xlarge.4 |
24 |
96 |
25/15 |
200 |
8 |
8 |
1 × T4 |
16 |
KVM |
g6.9xlarge.7 |
36 |
252 |
25/15 |
200 |
16 |
8 |
1 × T4 |
16 |
KVM |
g6.10xlarge.7 |
40 |
280 |
25/15 |
200 |
16 |
8 |
1 × T4 |
16 |
KVM |
g6.18xlarge.7 |
72 |
504 |
30/30 |
400 |
32 |
16 |
2 × T4 |
32 |
KVM |
g6.20xlarge.7 |
80 |
560 |
30/30 |
400 |
32 |
16 |
2 × T4 |
32 |
KVM |
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
Flavor |
vCPUs |
Memory (GiB) |
Max./Assured Bandwidth (Gbit/s) |
Max. PPS (10,000) |
Max. NIC Queues |
GPUs |
GPU Memory (GiB) |
Virtualization |
---|---|---|---|---|---|---|---|---|
g5.4xlarge.4 |
16 |
64 |
15/8 |
100 |
8 |
V100-8Q |
8 |
KVM |
g5.8xlarge.4 |
32 |
128 |
25/15 |
200 |
16 |
1 × V100 |
16 |
KVM |
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.
- 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)
- Graphics acceleration APIs
- DirectX 12, Direct2D, and DirectX Video Acceleration (DXVA)
- OpenGL 4.5
- Vulkan 1.0
- CUDA and OpenCL
- NVIDIA V100 GPUs
- Graphics applications accelerated
- Heavy-load CPU inference
- Automatic scheduling of G5 ECSs to AZs where NVIDIA V100 GPUs are used
- A maximum specification of 16 GiB of GPU memory and 4096 x 2160 resolution for processing graphics and videos
Supported Common Software
- AutoCAD
- 3DS MAX
- MAYA
- Agisoft PhotoScan
- ContextCapture
- Smart3D 3D modeling 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.
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.
- CPU: Intel® Xeon® E5-2697 v4 processors (2.3 GHz of base frequency and 3.5 GHz of turbo frequency)
- Provide professional graphics acceleration APIs
- NVIDIA M60 GPUs
- Graphics applications accelerated
- GPU passthrough
- Automatic scheduling of G3 ECSs to AZs where NVIDIA M60 GPUs are used
- A maximum specification of 16 GiB of GPU memory and 4096 x 2160 resolution for processing graphics and videos
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
Type |
vCPUs |
Memory (GiB) |
Flavor |
Virtualization |
GPUs |
GPU Memory (GiB) |
---|---|---|---|---|---|---|
Basic graphics processing G1 |
4 |
8 |
g1.xlarge |
Xen |
1 × M60-1Q |
1 |
8 |
16 |
g1.2xlarge |
Xen |
1 × M60-2Q |
2 |
|
16 |
32 |
g1.4xlarge |
Xen |
1 × M60-4Q |
4 |
Flavor |
vCPUs |
Memory (GiB) |
Max./Assured Bandwidth |
Max. PPS |
GPUs |
GPU Memory (GiB) |
Virtualization |
---|---|---|---|---|---|---|---|
g1.xlarge |
4 |
8 |
Medium |
Medium |
1 × M60-1Q |
1 |
Xen |
g1.xlarge.4 |
4 |
16 |
Medium |
Medium |
1 × M60-1Q |
1 |
Xen |
g1.2xlarge |
8 |
16 |
Medium |
Medium |
1 × M60-2Q |
2 |
Xen |
g1.2xlarge.8 |
8 |
64 |
Medium |
Medium |
Passthrough |
8 |
Xen |
g1.4xlarge |
16 |
32 |
Medium |
Medium |
1 × M60-4Q |
4 |
Xen |
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.
- CPU: Intel® Xeon® E5-2690 v4 processors (2.6 GHz of base frequency and 3.5 GHz of turbo frequency)
- NVIDIA M60 GPUs
- Graphics applications accelerated
- GPU hardware virtualization (vGPUs) and GPU passthrough
- Automatic scheduling of G1 ECSs to AZs where NVIDIA M60 GPUs are used
- A maximum specification of 8 GiB of GPU memory and 4096 x 2160 resolution for processing graphics and videos
- After a G1 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 G1 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.
- G1 ECSs do not support specifications change.
- g1.2xlarge.8 G1 ECSs do not support the remote login function provided by the cloud platform. To remotely log in to such an ECS, use MSTSC to log in to it and install VNC on the ECS.
Non-g1.2xlarge.8 G1 ECSs support remote login on the cloud platform. For details, see Login Using VNC.
- By default, G1 ECSs created using a public image have had the GRID driver of a specific version installed.
- If a G1 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.
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
Flavor |
vCPUs |
Memory (GiB) |
Max./Assured Bandwidth (Gbit/s) |
Max. PPS (10,000) |
Max. NIC Queues |
GPUs |
GPU Connection |
GPU Memory (GiB) |
Virtualization |
---|---|---|---|---|---|---|---|---|---|
p2vs.2xlarge.8 |
8 |
64 |
10/4 |
50 |
4 |
1 × V100 |
N/A |
1 × 32 GiB |
KVM |
p2vs.4xlarge.8 |
16 |
128 |
15/8 |
100 |
8 |
2 × V100 |
NVLink |
2 × 32 GiB |
KVM |
p2vs.8xlarge.8 |
32 |
256 |
25/15 |
200 |
16 |
4 × V100 |
NVLink |
4 × 32 GiB |
KVM |
p2vs.16xlarge.8 |
64 |
512 |
30/30 |
400 |
32 |
8 × V100 |
NVLink |
8 × 32 GiB |
KVM |
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.
- 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 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
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 |
---|---|---|---|---|---|---|---|---|---|---|
p2s.2xlarge.8 |
8 |
64 |
10/4 |
50 |
4 |
4 |
1 × V100 |
PCIe Gen3 |
1 × 32 GiB |
KVM |
p2s.4xlarge.8 |
16 |
128 |
15/8 |
100 |
8 |
8 |
2 × V100 |
PCIe Gen3 |
2 × 32 GiB |
KVM |
p2s.8xlarge.8 |
32 |
256 |
25/15 |
200 |
16 |
8 |
4 × V100 |
PCIe Gen3 |
4 × 32 GiB |
KVM |
p2s.16xlarge.8 |
64 |
512 |
30/30 |
400 |
32 |
8 |
8 × V100 |
PCIe Gen3 |
8 × 32 GiB |
KVM |
- 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
- 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
- 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.
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 or change its billing mode to yearly/monthly.
- By default, P2s ECSs created using a 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.
- GPU-accelerated ECSs do not support live migration.
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 |
---|---|---|---|---|---|---|---|---|---|---|
p2v.2xlarge.8 |
8 |
64 |
10/4 |
50 |
4 |
4 |
1 × V100 |
N/A |
1 × 16 GiB |
KVM |
p2v.4xlarge.8 |
16 |
128 |
15/8 |
100 |
8 |
8 |
2 × V100 |
NVLink |
2 × 16 GiB |
KVM |
p2v.8xlarge.8 |
32 |
256 |
25/15 |
200 |
16 |
8 |
4 × V100 |
NVLink |
4 × 16 GiB |
KVM |
p2v.16xlarge.8 |
64 |
512 |
30/30 |
400 |
32 |
8 |
8 × V100 |
NVLink |
8 × 16 GiB |
KVM |
- 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
- 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
- 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.
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 or change its billing mode to yearly/monthly.
- By default, P2v ECSs created using a public image have the Tesla driver installed.
- 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.
- GPU-accelerated ECSs do not support live migration.
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 |
---|---|---|---|---|---|---|---|---|---|---|
pi2.2xlarge.4 |
8 |
32 |
10/4 |
50 |
4 |
4 |
1 × T4 |
1 × 16 |
N/A |
KVM |
pi2.4xlarge.4 |
16 |
64 |
15/8 |
100 |
8 |
8 |
2 × T4 |
2 × 16 |
N/A |
KVM |
pi2.8xlarge.4 |
32 |
128 |
25/15 |
200 |
16 |
8 |
4 × T4 |
4 × 16 |
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.
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
Flavor |
vCPUs |
Memory (GiB) |
Max./Assured Bandwidth (Gbit/s) |
Max. PPS (10,000) |
Max. NIC Queues |
GPUs |
GPU Memory (GiB) |
Local Disks |
Virtualization |
---|---|---|---|---|---|---|---|---|---|
pi1.2xlarge.4 |
8 |
32 |
5/1.6 |
40 |
2 |
1 × P4 |
1 × 8 GiB |
N/A |
KVM |
pi1.4xlarge.4 |
16 |
64 |
8/3.2 |
70 |
4 |
2 × P4 |
2 × 8 GiB |
N/A |
KVM |
pi1.8xlarge.4 |
32 |
128 |
10/6.5 |
140 |
8 |
4 × P4 |
4 × 8 GiB |
N/A |
KVM |
- CPU: Intel® Xeon® E5-2697 v4 processors (2.3 GHz of base frequency and 3.5 GHz of turbo frequency)
- Up to four NVIDIA Tesla P4 GPUs on an ECS
- GPU hardware passthrough
- Up to 5.5 TFLOPS of single-precision computing on a single GPU
- Up to 22 TOPS of INT8 computing on a single GPU
- 8 GiB of ECC GPU memory with a bandwidth of 192 GiB/s on a single GPU
- Hardware video encoding and decoding engines embedded in GPUs for concurrent real-time 35-channel HD video transcoding and inference
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.
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