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Computing Instance

Last updated: 2022-05-09 16:27:35

    GPU Computing instances provide powerful computing capabilities to help you process a large number of concurrent computing tasks in real time. They are suitable for general computing scenarios such as deep learning and scientific computing. They provide a fast, stable, and elastic computing service and can be managed just like CVM instances.

    Use Cases

    High-performance graphics processing and 3D rendering, such as:

    • AI computing
    • Deep learning inference
    • Deep learning training
    • Scientific computing/HPC
    • Fluid dynamics
    • Molecular modeling
    • Meteorological engineering
    • Seismic analysis
    • Genomics

    Overview

    GPU Computing instances are available in the following types:

    Availability Instance GPU Type Available Image AZ
    Featured PNV4 NVIDIA A10
    • CentOS 7.2 or later
    • Ubuntu 16.04 or later
    • Windows Server 2016 or later
    Guangzhou, Shanghai, and Beijing
    GT4 NVIDIA A100 NVLink 40 GB
    GN10Xp NVIDIA Tesla V100 NVLink 32 GB
    • CentOS 7.2 or later
    • Ubuntu 14.04 or later
    • Windows Server 2012 or later
    Guangzhou, Shanghai, Nanjing, Beijing, Chengdu, Chongqing, Hong Kong (China), and Frankfurt
    GN7 NVIDIA Tesla T4 Guangzhou, Shanghai, Nanjing, Beijing, Chengdu, Chongqing, Hong Kong (China), Singapore, Silicon Valley, Mumbai, Virginia, and Frankfurt
    vGPU - NVIDIA Tesla T4
    • CentOS 8.0 64-bit GRID 11.1
    • Ubuntu 20.04 LTS 64-bit GRID 11.1
    Guangzhou, Shanghai, Nanjing, Beijing, Chengdu, Chongqing, Hong Kong (China), and Silicon Valley
    Available GI3X NVIDIA Tesla T4
    • CentOS 7.2 or later
    • Ubuntu 14.04 or later
    • Windows Server 2012 or later
    Guangzhou, Shanghai, Beijing, and Nanjing
    GN10X NVIDIA Tesla V100 NVLink 32 GB Guangzhou, Shanghai, Nanjing, Beijing, Chengdu, Chongqing, Singapore, Silicon Valley, Frankfurt, and Mumbai
    GN8 NVIDIA Tesla P40 Guangzhou, Shanghai, Beijing, Chengdu, Chongqing, Hong Kong (China), and Silicon Valley
    GN6
    GN6S
    NVIDIA Tesla P4
    • GN6: Chengdu
    • GN6S: Guangzhou, Shanghai, and Beijing
    Note

    AZ: Accurate to the city level. For more information, see the instance configuration information below.

    Suggestions on Computing Instance Model Selection

    Tencent Cloud provides diverse GPU Computing instances to meet business needs in different scenarios. Refer to the following tables to select a Computing instance as needed.

    The table below lists recommended GPU Computing instance models. A tick () indicates that the model supports the corresponding feature. A pentagram () indicates that the model is recommended.

    Feature/Instance PNV4 GT4 GN10Xp GN7 GI3X GN10X GN8 GN6
    GN6S
    Graphics and image processing -
    Video encoding and decoding -
    Deep learning training
    Deep learning inference
    Scientific computing - - - - -
    Note

    >- These recommendations are for reference only. Select an appropriate instance model based on your needs.

    • To use NVIDIA GPU instances for general computing tasks, you need to install the Tesla driver and CUDA toolkit. For more information, see Installing NVIDIA Driver and Installing CUDA Toolkit.
    • To use NVIDIA GPU instances for 3D rendering tasks such as high-performance graphics processing and video encoding and decoding, you must install a GRID driver and configure a license server.

    Service Options

    • Spot instances and pay-as-you-go instances are supported.
    • Instances can be launched in VPC.
    • Instances can be connected to other services such as CLB, without additional management and Ops costs. Private network traffic is free of charge.

    Instance Specification

    Computing PNV4

    Computing PNV4 supports not only general GPU computing tasks such as deep learning, but also graphics and image processing tasks such as 3D rendering and video encoding and decoding.

    Note

    This instance model is currently made available through an allowlist. To purchase it, submit a ticket for application.

    Use cases

    PNV4 is cost-effective and suitable for the following scenarios:

    • Deep learning inference and small-scale training scenarios, such as:
      • AI inference for mass deployment
      • Small-scale deep learning training
    • Graphics and image processing scenarios, such as:
      • Graphics and image processing
      • Video encoding and decoding
      • Graph database

    AZs

    PNV4 instances are available in Guangzhou Zone 7, Shanghai Zone 5, and Beijing Zone 6.

    Hardware specification

    • CPU: AMD EPYCTM Milan CPU 2.55 GHz, with a Max Boost frequency of 3.5 GHz.
    • GPU: NVIDIA® A10, providing 62.5 TFLOPS of single-precision floating point performance, 250 TOPS for INT8, and 500 TOPS for INT4.
    • Storage: Select the appropriate CBS cloud disk type. To expand the cloud disk capacity, create and mount an elastic cloud disk.
    • Network: Network optimization is enabled by default. The network performance of an instance depends on its specification. You can purchase public network bandwidth as needed.

    PNV4 instances are available in the following configurations:

    Model GPU
    (NVIDIA A10)
    GPU Video Memory
    (HBM2)
    vCPU Memory
    (DDR4)
    Private Network Bandwidth Packets In/Out
    (PPS)
    Number of Queues
    PNV4.7XLARGE116 1 1 * 24 GB 28 cores 116 GB 13 Gbps 2.3 million 28
    PNV4.14XLARGE232 2 2 * 24 GB 56 cores 232 GB 25 Gbps 4.7 million 48
    PNV4.28XLARGE466 4 4 * 24 GB 112 cores 466 GB 50 Gbps 9.5 million 48
    PNV4.56XLARGE932 8 8 * 24 GB 224 cores 932 GB 100 Gbps 19 million 48

    Computing GT4

    Computing GT4 instances are suitable for general GPU computing tasks such as deep learning and scientific computing.

    Use cases

    GT4 features powerful double-precision floating point computing capabilities. It is suitable for large-scale deep learning training and inference as well as scientific computing scenarios, such as:

    • Deep learning
    • High-performance database
    • Computational fluid dynamics
    • Computational finance
    • Seismic analysis
    • Molecular modeling
    • Genomics and others

    AZs

    GT4 instances are available in Guangzhou Zone 4, Shanghai Zone 4, and Beijing Zone 5.

    Hardware specification

    • CPU: AMD EPYC™ ROME CPU, with a clock rate of 2.6 GHz.
    • GPU: NVIDIA® A100 NVLink 40 GB, providing 19.5 TFLOPS of single-precision floating point performance, 9.7 TFLOPS of double-precision floating point performance, and 600 GB/s NVLink.
    • Memory: DDR4 with stable computing performance.
    • Storage: Select the appropriate CBS cloud disk type. To expand the cloud disk capacity, create and mount an elastic cloud disk.
    • Network: Private network bandwidth of up to 50 Gbps is supported, with strong packet sending/receiving capabilities. The network performance of an instance depends on its specification. You can purchase public network bandwidth as needed.

    GT4 instances are available in the following configurations:

    Model GPU
    (NVIDIA Tesla A100 NVLink 40 GB)
    GPU Video Memory
    (HBM2)
    vCPU Memory
    (DDR4)
    Private Network Bandwidth Packets In/Out
    (PPS)
    Number of Queues
    GT4.4XLARGE96 1 1 * 40 GB 16 cores 96 GB 5 Gbps 1.2 million 4
    GT4.8XLARGE192 2 40 GB x2 32 cores 192 GB 10 Gbps 2.35 million 8
    GT4.20XLARGE474 4 4 * 40 GB 82 cores 474 GB 25 Gbps 6 million 16
    GT4.41XLARGE948 8 8 * 40 GB 164 cores 948 GB 50 Gbps 12 million 32
    Note

    GPU driver: Drivers of NVIDIA Tesla 450 or later are required for NVIDIA A100 GPUs, and version 460.32.03 (Linux)/461.33 (Windows) are recommended. For more information on driver versions, see NVIDIA Driver Documentation.

    Computing GN10Xp

    Computing GN10Xp instances support not only general GPU computing tasks such as deep learning and scientific computing, but also graphics and image processing tasks such as 3D rendering and video encoding and decoding.

    Use cases

    GN10Xp features powerful double-precision floating point computing capabilities. It is suitable for the following scenarios:

    • Large-scale deep learning training and inference as well as scientific computing scenarios, such as:
      • Deep learning
      • High-performance database
      • Computational fluid dynamics
      • Computational finance
      • Seismic analysis
      • Molecular modeling
      • Genomics and others
    • Graphics and image processing scenarios, such as:
      • Graphics and image processing
      • Video encoding and decoding
      • Graph database

    AZs

    GN10Xp instances are available in Guangzhou Zones 3 and 4, Shanghai Zone 2, Nanjing Zone 1, Beijing Zone 5, Chengdu Zone 1, Chongqing Zone 1, Hong Kong Zone 2, and Frankfurt Zone 1.

    Hardware specification

    • CPU: Intel® Xeon® Platinum 8255C CPU, with a clock rate of 2.5 GHz.
    • **GPU: **NVIDIA® Tesla® V100 NVLink 32GB, providing 15.7 TFLOPS of single-precision floating point performance, 7.8 TFLOPS of double-precision floating point performance, 125 TFLOPS of deep learning accelerator performance with Tensor cores, and 300 GB/s NVLink.
    • Memory: DDR4, providing memory bandwidth up to 2,666 MT/s.
    • Storage: Select the appropriate CBS cloud disk type. To expand the cloud disk capacity, create and mount an elastic cloud disk.
    • Network: Network optimization is enabled by default. The network performance of an instance depends on its specification. You can purchase public network bandwidth as needed.

    GN10Xp instances are available in the following configurations:

    Model GPU
    (NVIDIA Tesla V100 NVLink 32 GB)
    GPU Video Memory
    (HBM2)
    vCPU Memory
    (DDR4)
    Private Network Bandwidth Packets In/Out
    (PPS)
    Number of Queues
    GN10Xp.2XLARGE40 1 1 * 32 GB 10 cores 40 GB 3 Gbps 0.8 million 2
    GN10Xp.5XLARGE80 2 2 * 32 GB 20 cores 80 GB 6 Gbps 1.5 million 5
    GN10Xp.10XLARGE160 4 4 * 32 GB 40 cores 160 GB 12 Gbps 2.5 million 10
    GN10Xp.20XLARGE320 8 8 * 32 GB 80 cores 320 GB 24 Gbps 4.9 million 16

    Computing GN7

    NVIDIA GN7 supports not only general GPU computing tasks such as deep learning, but also graphics and image processing tasks such as 3D rendering and video encoding and decoding.

    Use cases

    GN7 is cost-effective and suitable for the following scenarios:

    • Deep learning inference and small-scale training scenarios, such as:
      • AI inference for mass deployment
      • Small-scale deep learning training
    • Graphics and image processing scenarios, such as:
      • Graphics and image processing
      • Video encoding and decoding
      • Graph database

    AZs

    GN7 instances are available in the following AZs:

    • GN7.LARGE20 and GN7.2XLARGE40: Guangzhou Zones 3 and 4, Shanghai Zones 2 and 4, Nanjing Zone 1 and 2, Beijing Zones 3 and 5, Chengdu Zone 1, Chongqing Zone 1, and Silicon Valley Zone 2.
    • Other GN7 instances: Guangzhou Zones 3 and 4, Shanghai Zones 2 and 4, Nanjing Zones 1 and 2, Beijing Zones 3 and 5, Chengdu Zone 1, Chongqing Zone 1, Singapore Zone 1, Silicon Valley Zone 2, Hong Kong Zone 2, Mumbai Zone 2, Virginia Zone 2, and Frankfurt Zone 1.

    Hardware specification

    • CPU: Intel® Xeon® Platinum 8255C CPU, with a clock rate of 2.5 GHz.
    • GPU: NVIDIA® Tesla® T4, providing 8.1 TFLOPS of single-precision floating point performance, 130 TOPS for INT8, and 260 TOPS for INT4.
    • Memory: DDR4, providing memory bandwidth up to 2,666 MT/s.
    • Storage: Select the appropriate CBS cloud disk type. To expand the cloud disk capacity, create and mount an elastic cloud disk.
    • Network: Network optimization is enabled by default. The network performance of an instance depends on its specification. You can purchase public network bandwidth as needed.

    GN7 instances are available in the following configurations:

    Model GPU
    (NVIDIA
    Tesla T4)
    GPU Video Memory
    (HBM2)
    vCPU Memory
    (DDR4)
    Private Network Bandwidth Packets In/Out
    (PPS)
    Number of Queues
    GN7.LARGE20 1/4 4 GB vGPU 4 cores 20 GB 1.5 Gbps 0.5 million 8
    GN7.2XLARGE40 1/2 8 GB vGPU 10 cores 40 GB 3 Gbps 0.7 million 8
    GN7.2XLARGE32 1 1 * 16 GB 8 cores 32 GB 3 Gbps 0.6 million 8
    GN7.5XLARGE80 1 1 * 16 GB 20 cores 80 GB 7 Gbps 1.4 million 10
    GN7.8XLARGE128 1 1 * 16 GB 32 cores 128 GB 10 Gbps 2.4 million 16
    GN7.10XLARGE160 2 2 * 16 GB 40 cores 160 GB 13 Gbps 2.8 million 20
    GN7.20XLARGE320 4 4 * 16 GB 80 cores 320 GB 25 Gbps 5.6 million 32
    Note

    vGPU:

    • GN7 instance cluster provides vGPU-based instances. The vGPU type is vComputeServer, which only supports CUDA APIs.
    • vCS instances require a GRID driver and don't support Windows.

    Interference GI3X

    NVIDIA GI3X supports not only general GPU computing tasks such as deep learning, but also graphics and image processing tasks such as 3D rendering and video encoding and decoding.

    Use cases

    GI3X is cost-effective and suitable for the following scenarios:

    • Deep learning inference and small-scale training scenarios, such as:
      • AI inference for mass deployment
      • Small-scale deep learning training
    • Graphics and image processing scenarios, such as:
      • Graphics and image processing
      • Video encoding and decoding
      • Graph database

    AZs

    GI3X instances are available in Guangzhou Zone 3, Shanghai Zone 4, Nanjing Zones 1 and 2, and Beijing Zone 5.

    Hardware specification

    • CPU: AMD EPYC™ ROME CPU 2.6 GHz, with a Max Boost frequency of 3.3 GHz.
    • GPU: NVIDIA® Tesla® T4, providing 8.1 TFLOPS of single-precision floating point performance, 130 TOPS for INT8, and 260 TOPS for INT4.
    • Memory: Latest eight-channel DDR4 with stable computing performance.
    • Storage: Select the appropriate CBS cloud disk type. To expand the cloud disk capacity, create and mount an elastic cloud disk.
    • Network: Network optimization is enabled by default. The network performance of an instance depends on its specification. You can purchase public network bandwidth as needed.

    GI3X instances are available in the following configurations:

    Model GPU
    (NVIDIA
    Tesla T4)
    GPU Video Memory
    (GDDR6)
    vCPU Memory
    (DDR4)
    Private Network Bandwidth Packets In/Out
    (PPS)
    Number of Queues
    GI3X.8XLARGE64 1 1 * 16 GB 32 cores 64 GB 5 Gbps 1.4 million 8
    GI3X.22XLARGE226 2 2 * 16 GB 90 cores 226 GB 13 Gbps 3.75 million 16
    GI3X.45XLARGE452 4 4 * 16 GB 180 cores 452 GB 25 Gbps 7.5 million 32

    Computing GN10X

    Computing GN10X supports not only general GPU computing tasks such as deep learning and scientific computing, but also graphics and image processing tasks such as 3D rendering and video encoding and decoding.

    Use cases

    GN10X features powerful double-precision floating point computing capabilities. It is suitable for the following scenarios:

    • Large-scale deep learning training and inference as well as scientific computing scenarios, such as:
      • Deep learning
      • High-performance database
      • Computational fluid dynamics
      • Computational finance
      • Seismic analysis
      • Molecular modeling
      • Genomics and others
    • Graphics and image processing scenarios, such as:
      • Graphics and image processing
      • Video encoding and decoding
      • Graph database

    AZs

    GN10X instances are available in Guangzhou Zones 3 and 4, Shanghai Zones 2 and 3, Nanjing Zone 1, Beijing Zones 4 and 5, Chengdu Zone 1, Chongqing Zone 1, Singapore Zone 1, Silicon Valley Zone 2, Frankfurt Zone 1, and Mumbai Zone 2.

    Hardware specification

    • CPU: GN10X is configured with an Intel® Xeon® Gold 6133 CPU, with a clock rate of 2.5 GHz.
    • **GPU: **NVIDIA® Tesla® V100 NVLink 32GB, providing 15.7 TFLOPS of single-precision floating point performance, 7.8 TFLOPS of double-precision floating point performance, 125 TFLOPS of deep learning accelerator performance with Tensor cores, and 300 GB/s NVLink.
    • Memory: DDR4, providing memory bandwidth up to 2,666 MT/s.
    • Storage: Select the appropriate CBS cloud disk type. To expand the cloud disk capacity, create and mount an elastic cloud disk.
    • Network: Network optimization is enabled by default. The network performance of an instance depends on its specification. You can purchase public network bandwidth as needed.

    GN10X instances are available in the following configurations:

    Model GPU
    (NVIDIA Tesla V100 NVLink 32 GB)
    GPU Video Memory
    (HBM2)
    vCPU Memory
    (DDR4)
    Private Network Bandwidth Packets In/Out
    (PPS)
    Number of Queues
    GN10X.2XLARGE40 1 1 * 32 GB 8 cores 40 GB 3 Gbps 0.8 million 2
    GN10X.9XLARGE160 4 4 * 32 GB 36 cores 160 GB 13 Gbps 2.5 million 9
    GN10X.18XLARGE320 8 8 * 32 GB 72 cores 320 GB 25 Gbps 4.9 million 16

    Computing GN8

    NVIDIA GN8 supports not only general GPU computing tasks such as deep learning, but also graphics and image processing tasks such as 3D rendering and video encoding and decoding.

    Use cases

    GN8 is suitable for the following scenarios:

    • Deep learning training and inference scenarios, such as:
      • AI inference with high throughput
      • Deep learning
    • Graphics and image processing scenarios, such as:
      • Graphics and image processing
      • Video encoding and decoding
      • Graph database

    AZs

    GN8 instances are available in Hong Kong Zone 2, Guangzhou Zone 3, Shanghai Zone 3, Beijing Zones 2 and 4, Chengdu Zone 1, Chongqing Zone 1, and Silicon Valley Zone 1.

    Hardware specification

    • CPU: Intel® Xeon® E5-2680 v4 CPU, with a clock rate of 2.4 GHz.
    • GPU: NVIDIA® Tesla® P40, providing 12 TFLOPS of single-precision floating point performance and 47 TOPS for INT8.
    • Memory: DDR4, providing memory bandwidth up to 2,666 MT/s.
    • Storage: Select the appropriate CBS cloud disk type. To expand the cloud disk capacity, create and mount an elastic cloud disk.
    • Network: Network optimization is enabled by default. The network performance of an instance depends on its specification. You can purchase public network bandwidth as needed.

    GN8 instances are available in the following configurations:

    Model GPU
    (NVIDIA Tesla P40)
    GPU Video Memory
    (HBM2)
    vCPU Memory
    (DDR4)
    Private Network Bandwidth Packets In/Out
    (PPS)
    Number of Queues
    GN8.LARGE56 1 24 GB 6 cores 56 GB 1.5 Gbps 0.45 million 8
    GN8.3XLARGE112 2 48 GB 14 cores 112 GB 2.5 Gbps 0.5 million 8
    GN8.7XLARGE224 4 96 GB 28 cores 224 GB 5 Gbps 0.7 million 14
    GN8.14XLARGE448 8 192 GB 56 cores 448 GB 10 Gbps 0.7 million 28

    Computing GN6 and GN6S

    NVIDIA GN6 and GN6S support not only general GPU computing tasks such as deep learning, but also graphics and image processing tasks such as 3D rendering and video encoding and decoding.

    Use cases

    GN6 and GN6S are cost-effective and suitable for the following scenarios:

    • Deep learning inference and small-scale training scenarios, such as:
      • AI inference for mass deployment
      • Small-scale deep learning training
    • Graphics and image processing scenarios, such as:
      • Graphics and image processing
      • Video encoding and decoding
      • Graph database

    AZs

    GN6 and GN6S instances are available in the following AZs:

    • GN6: Chengdu Zone 1.
    • GN6S: Guangzhou Zone 3, Shanghai Zones 2, 3, and 4, and Beijing Zones 4 and 5.

    Hardware specification

    • CPU: GN6 is configured with an Intel® Xeon® E5-2680 v4 CPU, with a clock rate of 2.4 GHz. GN6S is configured with an Intel® Xeon® Silver 4110 CPU, with a clock rate of 2.1 GHz.
    • GPU: NVIDIA® Tesla® P4, providing 5.5 TFLOPS of single-precision floating point performance and 22 TOPS for INT8.
    • Memory: DDR4, providing memory bandwidth up to 2,666 MT/s.
    • Storage: Select the appropriate CBS cloud disk type. To expand the cloud disk capacity, create and mount an elastic cloud disk.
    • Network: Network optimization is enabled by default. The network performance of an instance depends on its specification. You can purchase public network bandwidth as needed.

    GN6 and GN6S instances are available in the following configurations:

    Model GPU
    (NVIDIA Tesla P4)
    GPU Video Memory
    (HBM2)
    vCPU Memory
    (DDR4)
    Private Network Bandwidth Packets In/Out
    (PPS)
    Number of Queues
    GN6.7XLARGE48 1 8 GB 28 cores 48 GB 5 Gbps 1.2 million 14
    GN6.14XLARGE96 2 16 GB 56 cores 96 GB 10 Gbps 1.2 million 28
    GN6S.LARGE20 1 8 GB 4 cores 20 GB 5 Gbps 0.5 million 8
    GN6S.2XLARGE40 2 16 GB 8 cores 40 GB 9 Gbps 0.8 million 8
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