tencent cloud

Cloud GPU Service

Cloud GPU Service is an elastic computing service that provides GPU computing power with high-performance parallel computing capabilities. As a powerful tool at the IaaS layer, it delivers high computing power for deep learning training, scientific computing, graphics and image processing, video encoding and decoding, and other highly intensive workloads.

Strengths
Ultimate Parallel Computing Capabilities
Ultimate Parallel Computing Capabilities

Improve your business efficiency and competitiveness with high-performance parallel computing capabilities

Quick Environment Deployment
Quick Environment Deployment

Set up your deployment environment quickly with auto-installed GPU drivers, CUDA, and cuDNN and preinstalled driver images

Native Acceleration Engine
Native Acceleration Engine

Accelerate distributed training and inference by using TACO Kit, an out-of-the-box computing acceleration engine provided by Tencent Cloud

Specification Recommendation
spot instance
GPU Computing GN7
Intel Xeon Cascade Lake 8255C(2.5 GHz) + NVIDIA® Tesla T4
CPU
8-core
Memory
32GB
GPU
1
Packet sending/receiving capabilities
600,000 PPS
$0.204
/hour
Buy Now
GPU Computing GN7
spot instance
$0.204
/hour
Buy Now
Intel Xeon Cascade Lake 8255C(2.5 GHz) + NVIDIA® Tesla T4
CPU
8-core
Memory
32GB
GPU
1
Packet sending/receiving capabilities
600,000 PPS
Pay-As-You-Go
GPU Computing GN10Xp
Intel Xeon Cascade Lake 8255C(2.5 GHz) + NVIDIA® Tesla® V100 NVLink 32GB
CPU
80-core
Memory
320GB
GPU
8
Packet sending/receiving capabilities
4.9 million PPS
$23.54
/hour
Buy Now
GPU Computing GN10Xp
Pay-As-You-Go
$23.54
/hour
Buy Now
Intel Xeon Cascade Lake 8255C(2.5 GHz) + NVIDIA® Tesla® V100 NVLink 32GB
CPU
80-core
Memory
320GB
GPU
8
Packet sending/receiving capabilities
4.9 million PPS
Pay-As-You-Go
GPU Computing GN10Xp
Intel Xeon Cascade Lake 8255C(2.5 GHz) + NVIDIA® Tesla® V100 NVLink 32GB
CPU
80-core
Memory
320GB
GPU
8
Packet sending/receiving capabilities
4.9 million PPS
$23.54
/hour
Buy Now
GPU Computing GN10Xp
Pay-As-You-Go
$23.54
/hour
Buy Now
Intel Xeon Cascade Lake 8255C(2.5 GHz) + NVIDIA® Tesla® V100 NVLink 32GB
CPU
80-core
Memory
320GB
GPU
8
Packet sending/receiving capabilities
4.9 million PPS
spot instance
GPU Computing GN7
Intel Xeon Cascade Lake 8255C(2.5 GHz) + NVIDIA® Tesla T4
CPU
8-core
Memory
32GB
GPU
1
Packet sending/receiving capabilities
600,000 PPS
$0.204
/hour
Buy Now
GPU Computing GN7
spot instance
$0.204
/hour
Buy Now
Intel Xeon Cascade Lake 8255C(2.5 GHz) + NVIDIA® Tesla T4
CPU
8-core
Memory
32GB
GPU
1
Packet sending/receiving capabilities
600,000 PPS
spot instance
GPU Rendering GN7vw
Intel Xeon Cascade Lake 8255C(2.5 GHz) + NVIDIA® Tesla T4
CPU
16-core
Memory
64GB
GPU
1
Packet sending/receiving capabilities
1.5 million PPS
$0.242
/hour
Buy Now
GPU Rendering GN7vw
spot instance
$0.242
/hour
Buy Now
Intel Xeon Cascade Lake 8255C(2.5 GHz) + NVIDIA® Tesla T4
CPU
16-core
Memory
64GB
GPU
1
Packet sending/receiving capabilities
1.5 million PPS
spot instance
GPU Computing GN7
Intel Xeon Cascade Lake 8255C(2.5 GHz) + NVIDIA® Tesla T4
CPU
8-core
Memory
32GB
GPU
1
Packet sending/receiving capabilities
600,000 PPS
$0.204
/hour
Buy Now
GPU Computing GN7
spot instance
$0.204
/hour
Buy Now
Intel Xeon Cascade Lake 8255C(2.5 GHz) + NVIDIA® Tesla T4
CPU
8-core
Memory
32GB
GPU
1
Packet sending/receiving capabilities
600,000 PPS
Pay-As-You-Go
GPU Computing GN10Xp
Intel Xeon Cascade Lake 8255C(2.5 GHz) + NVIDIA® Tesla® V100 NVLink 32GB
CPU
80-core
Memory
320GB
GPU
8
Packet sending/receiving capabilities
4.9 million PPS
$23.54
/hour
Buy Now
GPU Computing GN10Xp
Pay-As-You-Go
$23.54
/hour
Buy Now
Intel Xeon Cascade Lake 8255C(2.5 GHz) + NVIDIA® Tesla® V100 NVLink 32GB
CPU
80-core
Memory
320GB
GPU
8
Packet sending/receiving capabilities
4.9 million PPS
Pay-As-You-Go
GPU Computing GN10Xp
Intel Xeon Cascade Lake 8255C(2.5 GHz) + NVIDIA® Tesla® V100 NVLink 32GB
CPU
80-core
Memory
320GB
GPU
8
Packet sending/receiving capabilities
4.9 million PPS
$23.54
/hour
Buy Now
GPU Computing GN10Xp
Pay-As-You-Go
$23.54
/hour
Buy Now
Intel Xeon Cascade Lake 8255C(2.5 GHz) + NVIDIA® Tesla® V100 NVLink 32GB
CPU
80-core
Memory
320GB
GPU
8
Packet sending/receiving capabilities
4.9 million PPS
spot instance
GPU Computing GN7
Intel Xeon Cascade Lake 8255C(2.5 GHz) + NVIDIA® Tesla T4
CPU
8-core
Memory
32GB
GPU
1
Packet sending/receiving capabilities
600,000 PPS
$0.204
/hour
Buy Now
GPU Computing GN7
spot instance
$0.204
/hour
Buy Now
Intel Xeon Cascade Lake 8255C(2.5 GHz) + NVIDIA® Tesla T4
CPU
8-core
Memory
32GB
GPU
1
Packet sending/receiving capabilities
600,000 PPS
spot instance
GPU Rendering GN7vw
Intel Xeon Cascade Lake 8255C(2.5 GHz) + NVIDIA® Tesla T4
CPU
16-core
Memory
64GB
GPU
1
Packet sending/receiving capabilities
1.5 million PPS
$0.242
/hour
Buy Now
GPU Rendering GN7vw
spot instance
$0.242
/hour
Buy Now
Intel Xeon Cascade Lake 8255C(2.5 GHz) + NVIDIA® Tesla T4
CPU
16-core
Memory
64GB
GPU
1
Packet sending/receiving capabilities
1.5 million PPS
Solutions

All-True Internet Solution

For rendering, gaming, virtual humans, and NFT art

Rendering Solution

Ease of use, workflow automation, and comprehensive monitoring

AI-Enabled Moderation Solution for Game Live Streaming Platform

Rapidly scale up to meet your business requirements

Autonomous Driving Solution

Proccess massive data in the most cost-effective way

All-True Internet Solution

All-True Internet Solution

Customer challenges:

To deliver a highly immersive and vivid experience, virtual worlds rely on powerful computing for rendering and other highly demanding workloads. However, most mobile phones and other end-user devices don't have the hardware performance to sustain intense rendering. Moreover, software packages that contain the required rendering engines and other various materials often reach gigabytes in size, occupying massive storage space on the user’s device.

Solution:

With Tencent Cloud's powerful GPU computing power, this solution integrates enterprise-grade rendering, qGPU-based container-level resource splitting and virtualization technologies, video encoding and decoding technologies, and cloud-based streaming solutions. It delivers high computing power for rendering in the cloud, so end users only need to connect to the network to access high-performance rendering. This frees up the resources and storage on end-user devices and delivers a smooth cloud-edge integration experience.

Benefits of cloud deployment:

The cloud native-based solution allows for canary release and rapid launch of your business. Elastic scaling allows you to schedule massive numbers of resources, so you can easily scale your business to adapt to peak and off-peak hours. Combined with the qGPU virtualization sharing technology featuring GPU computing power and VRAM isolation, it can greatly increase GPU utilization while reducing your enterprise costs.

The new-generation Cloud GPU Service provides high-density encoding computing power and a higher network performance. Joining hands with NVIDIA, it is China's first one-stop CloudXR solution and guarantees smooth, lightweight user experience for VR applications.

Related Products

Rendering Solution

Rendering Solution

Customer challenges:

In industries such as movie, TV, advertising, and architectural planning, content creators and post-production teams rely on a large number of machines to complete the rendering work related to visual effects, 3D animations, and design sketches.

Traditional IDC resources often struggle to meet high rendering demands during occasional peak hours, and idle resources are wasted during off-peak hours. This means that upfront investment costs are very high, while return on investment is slow, making it difficult for the enterprise business to expand.

Solution:

Tencent provides various professional GPU Rendering instances, which work with BatchCompute to enable teams to automate their content rendering workflows. Creative and technical professionals can build their own rendering-dependent processes by leveraging Cloud GPU Service's massive computing resources and BatchCompute's job scheduling capabilities to complete their visual creation projects more efficiently.

Benefits of cloud deployment:

Cloud GPU Service allows you to quickly create and manage clusters and select the GPU models and quantities you need to fully sustain your rendering needs.

Jobs are configurable, easy to use, and reusable. You can set up a job flow that is specifically tailored to your process and rendering logic, all in the cloud.

Cloud GPU Service provides a multidimensional job and resource monitoring system, eliminating the need to worry about Ops at the IaaS layer.

Related Products

AI-Enabled Moderation Solution for Game Live Streaming Platform

AI-Enabled Moderation Solution for Game Live Streaming Platform

Customer challenges:

The business was growing, and the self-built cluster had a long scaling period, which could hardly sustain the ever-increasing video moderation tasks.

The hardware devices in the self-built cluster were old, and the computing, storage, and network performance could no longer sustain the business's requirements for high concurrency and low latency.


Solution:

The large-scale GPU-based inference cluster supports a massive number of data samples. A cluster can be scaled quickly to sustain a high number of concurrent requests, eliminating performance bottlenecks.

The high-performance training cluster works with Turbo high-throughput storage to quickly train and iterate models, increasing the video moderation accuracy as well as the moderation success rate.


Benefits of cloud deployment:

Cloud GPU Service supports elastic scaling, so you can quickly adjust the service scale based on your current business needs. You can purchase and utilize computing power when you need it, allowing you to vastly reduce your upfront investment costs.

Related Products

Autonomous Driving Solution

Autonomous Driving Solution

Customer challenges:

Autonomous driving systems collect data through in-vehicle sensors and cameras, which generate several terabytes of data every day. These massive amounts of data need to be analyzed and processed quickly and stored persistently. Therefore, a large-scale computing cluster with high-IOPS, high-performance storage, and high-bandwidth network infrastructure is required to fully satisfy the computing and storage needs at different stages such as annotation, training, and simulation.


Solution:

Cloud GPU Service provides a CPM High-Performance Computing cluster enabled by powerful V100 and A100 GPUs, which allow it to sustain the high-performance computing required by autonomous driving systems.

The High-Performance Computing cluster supports interconnection over a 100 GiB RDMA network. It can be used with GooseFS to improve the efficiency of large-scale distributed clusters used for training.

COS stores data in a cross-infrastructure, multi-device, and redundant manner and provides remote disaster recovery and resource isolation capabilities to guarantee data durability and security.


Benefits of cloud deployment:

Cloud GPU Service allows you to run large-scale parallel simulations with high-elasticity and cost-effective storage. It provides full-linkage services that enable automakers and R&D teams to develop and optimize autonomous driving technology more quickly at lower costs.

Related Products
Solutions
All-True Internet Solution

Customer challenges:

To deliver a highly immersive and vivid experience, virtual worlds rely on powerful computing for rendering and other highly demanding workloads. However, most mobile phones and other end-user devices don't have the hardware performance to sustain intense rendering. Moreover, software packages that contain the required rendering engines and other various materials often reach gigabytes in size, occupying massive storage space on the user’s device.

Solution:

With Tencent Cloud's powerful GPU computing power, this solution integrates enterprise-grade rendering, qGPU-based container-level resource splitting and virtualization technologies, video encoding and decoding technologies, and cloud-based streaming solutions. It delivers high computing power for rendering in the cloud, so end users only need to connect to the network to access high-performance rendering. This frees up the resources and storage on end-user devices and delivers a smooth cloud-edge integration experience.

Benefits of cloud deployment:

The cloud native-based solution allows for canary release and rapid launch of your business. Elastic scaling allows you to schedule massive numbers of resources, so you can easily scale your business to adapt to peak and off-peak hours. Combined with the qGPU virtualization sharing technology featuring GPU computing power and VRAM isolation, it can greatly increase GPU utilization while reducing your enterprise costs.

The new-generation Cloud GPU Service provides high-density encoding computing power and a higher network performance. Joining hands with NVIDIA, it is China's first one-stop CloudXR solution and guarantees smooth, lightweight user experience for VR applications.

Related Products
Rendering Solution

Customer challenges:

In industries such as movie, TV, advertising, and architectural planning, content creators and post-production teams rely on a large number of machines to complete the rendering work related to visual effects, 3D animations, and design sketches.

Traditional IDC resources often struggle to meet high rendering demands during occasional peak hours, and idle resources are wasted during off-peak hours. This means that upfront investment costs are very high, while return on investment is slow, making it difficult for the enterprise business to expand.

Solution:

Tencent provides various professional GPU Rendering instances, which work with BatchCompute to enable teams to automate their content rendering workflows. Creative and technical professionals can build their own rendering-dependent processes by leveraging Cloud GPU Service's massive computing resources and BatchCompute's job scheduling capabilities to complete their visual creation projects more efficiently.

Benefits of cloud deployment:

Cloud GPU Service allows you to quickly create and manage clusters and select the GPU models and quantities you need to fully sustain your rendering needs.

Jobs are configurable, easy to use, and reusable. You can set up a job flow that is specifically tailored to your process and rendering logic, all in the cloud.

Cloud GPU Service provides a multidimensional job and resource monitoring system, eliminating the need to worry about Ops at the IaaS layer.

Related Products
AI-Enabled Moderation Solution for Game Live Streaming Platform

Customer challenges:

The business was growing, and the self-built cluster had a long scaling period, which could hardly sustain the ever-increasing video moderation tasks.

The hardware devices in the self-built cluster were old, and the computing, storage, and network performance could no longer sustain the business's requirements for high concurrency and low latency.


Solution:

The large-scale GPU-based inference cluster supports a massive number of data samples. A cluster can be scaled quickly to sustain a high number of concurrent requests, eliminating performance bottlenecks.

The high-performance training cluster works with Turbo high-throughput storage to quickly train and iterate models, increasing the video moderation accuracy as well as the moderation success rate.


Benefits of cloud deployment:

Cloud GPU Service supports elastic scaling, so you can quickly adjust the service scale based on your current business needs. You can purchase and utilize computing power when you need it, allowing you to vastly reduce your upfront investment costs.

Related Products
Autonomous Driving Solution

Customer challenges:

Autonomous driving systems collect data through in-vehicle sensors and cameras, which generate several terabytes of data every day. These massive amounts of data need to be analyzed and processed quickly and stored persistently. Therefore, a large-scale computing cluster with high-IOPS, high-performance storage, and high-bandwidth network infrastructure is required to fully satisfy the computing and storage needs at different stages such as annotation, training, and simulation.


Solution:

Cloud GPU Service provides a CPM High-Performance Computing cluster enabled by powerful V100 and A100 GPUs, which allow it to sustain the high-performance computing required by autonomous driving systems.

The High-Performance Computing cluster supports interconnection over a 100 GiB RDMA network. It can be used with GooseFS to improve the efficiency of large-scale distributed clusters used for training.

COS stores data in a cross-infrastructure, multi-device, and redundant manner and provides remote disaster recovery and resource isolation capabilities to guarantee data durability and security.


Benefits of cloud deployment:

Cloud GPU Service allows you to run large-scale parallel simulations with high-elasticity and cost-effective storage. It provides full-linkage services that enable automakers and R&D teams to develop and optimize autonomous driving technology more quickly at lower costs.

Related Products
Success Stories

Cloud GPU Service is an elastic computing service that provides GPU computing power and has high-performance parallel computing capabilities. It offers easily accessible computing power, effectively relieving your computing pressure, improving your business efficiency and competitiveness, and empowering your business success.

Ubitus
Ubitus
Ubitus' cloud game service leverages a distributed service-oriented architecture to accelerate high numbers of compute-intensive tasks in the cloud, such as multimedia conversion and game image compression. Tencent Cloud provides diverse GPU instance specifications and storage resources to adapt to Ubitus' business load, helping it increase the utilization and achieve operating goals at lower costs.
WeBank
WeBank
WeBank's face recognition-based identity verification technology relies on Cloud GPU Service, which deploys large-scale inference clusters to respond to verification requests in real time. This well solves the biggest problem in financial service efficiency optimization.
WeChat
WeChat
The massive amount of data on WeChat makes the compute resource usage and training time soar. The original CPU clusters generally would need several hours to complete a training task, seriously compromising the service iteration speed. In contrast, by using multi-instance multi-card distributed GPU training, a task can be completed in minutes now.
FAQs
In what cases should I use a GPU instance?

A GPU has more arithmetic logic units (ALUs) than a CPU and supports large-scale multi-thread parallel computing. It is most suitable for the following use cases:

  • AI computing: Deep learning inference and training
  • Graphics and image processing: Cloud game, cloud phone, cloud desktop, and CloudXR
  • High-performance computing: Fluid dynamics, molecular modeling, meteorological engineering, seismic analysis, genomics, etc.
How do I select a GPU instance model?

You need to select an instance model based on your use case:

  • AI training: GN10Xp, GN10X, GT4, GN8, and GN6/GN6S
  • AI inference: GN7, GN10Xp, GN10X, PNV4, GI3X, GN6, and GN6S
  • Graphics and image processing: GN7vw, GNV4, GNV4v, and GI1
  • Scientific computing: GN10Xp, GN10X, GT4, and GI3X
  • For more information, see Computing Instance and Rendering Instance.
How do I select a driver based on the instance model and scenario?

NVIDIA GPU instance models include physical passthrough instances (having entire GPUs) and vGPU instances (having no entire GPUs, such as 1/4 GPU).

The GPU on a physical passthrough instance can use the Tesla or GRID driver (several models don't support the GRID driver) to accelerate computing in different scenarios.

A vGPU can use only the GRID driver on certain versions to accelerate computing.

For detailed directions on how to install a driver on an NVIDIA GPU instance, see Installing NVIDIA Tesla Driver.

Does Cloud GPU Service allow you to adjust the configuration of an instance?

GPU instance models PNV4, GT4, GN10X, GN10Xp, GN6, GN6S, GN7, GN8, GNV4v, GNV4, GN7vw, and GI1 support instance configuration adjustment in the same instance family, while GI3X does not support instance configuration adjustment.

What should I do if the resources are sold out when I purchase an instance?

You can try the following:

  • Change the region
  • Change the AZ
  • Change the resource configuration

If the problem persists, please contact us.

How are GPU instances billed?

GPU instances are pay-as-you-go, where the instances are billed by second and settled hourly. You can purchase or release the instances at any time. For more information, see Pricing Overview.

How can I get the estimated price of GPU instances?

When you purchase GPU instances, you can check the estimated price in the Price Calculator.

What is the difference between a private IP and a public IP of a GPU instance?

A private IP is a connection address that provides services for a client with a source IP from the private network. A public IP is a connection address that enables public network communication for a client with a source IP from the public network. They can be directly mapped to each other through network address translation. GPU instances in the same region can communicate over the private network, while those in different regions can only communicate over the public network.

What is an EIP?

An EIP is a static IP specifically designed for dynamic cloud computing. It is region-specific. You can quickly remap an EIP to another GPU instance (or a CVM/NAT gateway instance) under your account to block instance failures. For more information, see EIP.

What storage options does Cloud GPU Service offer?

Tencent Cloud provides different types of data storage options for GPU instances, including cloud disk, local disk, COS, and block storage device mapping. Different options differ in performance and price and are suitable for different use cases. For more information, see Storage Overview.

What storage options does a CPM GPU instance offer?

Some of the CPM GPU instance models support local storage, and you can use remote storage as needed.

1. Local storage

Certain CPM GPU instances are equipped with NVMe SSD disks with excellent read/write performance, which is three times higher than that of a general model, so as to guarantee the stability of high-performance computing jobs.

2. Remote storage

CFS: You can mount Turbo CFS through the smart ENI technology, which enables flexible expansion of storage capacity and guarantees strong consistency between three replicas.

COS: COS works with the distributed cluster architecture of GooseFS to improve the data locality and uses the high-speed cache feature to improve the storage performance and increase the bandwidth for writing data to COS.

How do I back up data in a GPU instance?

1. If your GPU instance uses a cloud disk, you can back up your business data by creating a custom system disk image and a data disk snapshot

2. If your GPU instance uses a local disk, you can back up your business data by creating a custom system disk image. However, you need to customize backup policies for the business data in your data disk. Usually, you can back up the data in your GPU instance over FTP. For more information on FTP deployment, see the following documents:

3. In addition, if you require high data security, you can purchase more professional third-party custom backup services.

What is a region?

Tencent Cloud regions are completely isolated. This guarantees the maximum cross-region stability and fault tolerance. We will gradually deploy nodes in more regions for a wider coverage. We recommend you select the region closest to your end users to minimize the access latency and improve the download speed. For more information on supported regions, see Regions and AZs.

How do I select an appropriate region?

We recommend you select the region closest to your users, and choose the same region for your GPU instances so that communication can occur over the private network.

  • Close to your user: By selecting a region that is geographically close to your users, you can reduce their access latency and increase their access speed. For example, if most of your users are located near Southeast Asia, Singapore or Thailand will be a good choice.
  • Intra-region communication: GPU instances in the same region can communicate over private network free of charge. When they are in different regions, they can only communicate over the public network, which incurs fees. To enable instances to communicate over the private network, you must select the same region for them.
How are AZs isolated from each other?

Each AZ runs on its own independent and physically distinct infrastructure and is designed to be highly reliable. AZs do not share common vulnerable equipment such as power supplies and cooling equipment. In addition, they are physically independent of each other, so if a natural disaster such as a fire, tornado, or flood occurs, only the AZ at that location would be affected while other AZs remain operational.

Where can I find more information on security?

Tencent Cloud provides various network and security services such as security groups, encrypted login, and EIP to ensure the secure, efficient, and free operations of your instances. For more information on GPU security, see Network and Security Overview.

How do I prevent others from viewing my system?

You can control the access to your GPU instances by adding them into a security group. You can also configure communication across security groups and specify which IP subnets can communicate with your instances.

How do I troubleshoot security issues?

When security risks are detected, you can troubleshoot the issues as instructed in Security Violation Handling Guide and resolve it as instructed in Cloud Workload Protection.

Does Cloud GPU Service provide images with a preinstalled GPU driver?

If you select a vGPU or Rendering instance, you can select an image with a preinstalled GRID driver in “Public images” on the purchase page.

What types of images are available?

Tencent Cloud provides public images, shared images, and custom images. For more information, see Image Types. You can select appropriate images based on their different features.

What is a shared image?

You can share a custom image with other users, or obtain images shared by others. For more information on the limits and usage of shared images, see Sharing Custom Images.

How many users can I share an image with?

An image can be shared with up to 50 users. Shared images do not count towards your own image quota.

Tencent Cloud GPU Service

An Ultimate Parallel Computing Capabilities and Professional Computing Acceleration Service. We are committed to providing you with a personalized presales consultation service.

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