Help Center/ Elastic Cloud Server/ User Guide (ME-Abu Dhabi Region)/ Service Overview/ x86 ECS Specifications and Types/ GPU-accelerated ECSs
Updated on 2024-07-24 GMT+08:00
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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 ECS Types

Helpful links:
Table 1 GPU-accelerated ECSs

Type

Series

GPU

CUDA Cores per GPU

Single-GPU Performance

Application

Computing-accelerated

P2s

NVIDIA V100

5,120
  • 14 TFLOPS of single-precision floating-point computing
  • 7 TFLOPS of double-precision floating-point computing
  • 112 TFLOPS tensor core for deep learning acceleration

AI deep learning training, scientific computing, computational fluid dynamics, computational finance, seismic analysis, molecular modeling, and genomics.

Inference-accelerated

Pi2

NVIDIA T4 (GPU passthrough)

2,560
  • 8.1 TFLOPS of single-precision floating-point computing
  • 130 INT8 TOPS
  • 260 INT4 TOPS

Machine learning, deep learning, inference training, scientific computing, seismic analysis, computing finance, rendering, multimedia encoding and decoding

Inference-accelerated

Pi2nl

NVIDIA P4 (GPU passthrough)

2,560
  • 8.1 TFLOPS of single-precision floating-point computing
  • 130 INT8 TOPS
  • 260 INT4 TOPS

Machine learning, deep learning, inference training, scientific computing, seismic analysis, computing finance, rendering, multimedia encoding and decoding

Images Supported by GPU-accelerated ECSs

Table 2 Images supported by GPU-accelerated ECSs

Type

Series

Supported Image

Computing-accelerated

P2s
  • Windows Server 2016 Standard 64bit

Inference-accelerated

Pi2
  • CentOS 7.5 64bit
  • Windows Server 2019 Standard 64bit
  • Windows Server 2016 Standard 64bit

Inference-accelerated

Pi2nl
  • CentOS 7.5 64bit
  • Ubuntu 16.04 Server 64bit
  • Windows Server 2016 Standard 64bit

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

Table 3 P2s ECS 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
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.

    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 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.

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

Table 4 Pi2 ECS specifications

Flavor

vCPUs

Memory

(GiB)

Max./Assured Bandwidth (Gbit/s)

Max. PPS

(10,000)

Max. NIC Queues

GPUs

GPU Memory

(GiB)

Local Disks

Virtualization

pi2.2xlarge.4

8

32

10/4

50

4

1 × T4

1 × 16

N/A

KVM

pi2.4xlarge.4

16

64

15/8

100

8

2 × T4

2 × 16

N/A

KVM

pi2.8xlarge.4

32

128

25/15

200

16

4 × T4

4 × 16

N/A

KVM

pi2.16xlarge.4

64

256

30/30

400

32

8 × T4

8 × 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.

  • 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.

Inference-accelerated Pi2nl

Overview

Pi2nl 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 Pi2nl ECSs can also be used for light-workload training.

Specifications
Table 5 Pi2nl ECS specifications

Flavor

vCPUs

Memory

(GiB)

Max./Assured Bandwidth

(Gbit/s)

Max. PPS

(10,000)

Max. NIC Queues

GPUs

GPU Memory

(GiB)

Local Disks

Virtualization

pi2nl.2xlarge.4

8

32

10/4

50

4

1 × T4

1 × 16

N/A

KVM

pi2nl.4xlarge.4

16

64

15/8

100

8

2 × T4

2 × 16

N/A

KVM

pi2nl.8xlarge.4

32

128

25/15

200

16

4 × T4

4 × 16

N/A

KVM

pi2nl.16xlarge.4

64

256

30/30

400

32

8 × T4

8 × 16

N/A

KVM

Pi2nl 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

Pi2nl ECSs are used in GPU-based inference computing scenarios, such as image recognition, speech recognition, and natural language processing. The Pi2nl 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 Pi2nl 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 Pi2nl 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.

  • Pi2nl ECSs support automatic recovery when the hosts accommodating such ECSs become faulty.
  • By default, Pi2nl ECSs created using a public image have the Tesla driver installed.
  • If a Pi2nl ECS is created using a private image, make sure that the Tesla driver was installed during the private image creation. If the Tesla driver has not been installed, 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.