Cloud GPU Service is a fast, stable, and auto scaling computing solution powered by GPUs. It is primarily used in scenarios such as deep learning training and inference, graphics and image processing, and scientific computing. Cloud GPU Service offers the same convenient and efficient management as standard CVM instances. With its powerful capability to process massive amounts of data quickly, it effectively alleviates users’ computational workload, enhancing business efficiency and competitiveness.
Why Choose Cloud GPU Service?
Comparison between Cloud GPU Service and self-built GPU servers:
Strengths
Cloud GPU Service
Self-Built GPU Server
Elasticity
Obtain one or multiple high-performance computing instances within minutes.
Flexibly customize instances on demand, with one-click upgrades to higher performance and capacity specifications, enabling rapid and smooth scaling to meet fast-growing business needs.
Fixed machine configurations make it challenging to meet evolving demands.
Achieve peak single-machine computing power of up to 125.6 TFlops for single-precision floating-point operations and 62.4 TFlops for double-precision floating point operations.
Manual disaster recovery relies on hardware robustness.
Single point of physical data leads to uncontrollable data security
Easy to Use
Seamless integration with various Tencent Cloud products, such as CVM and CLB, with free private network traffic.
Managed in the same way as CVM, eliminating the need for a jump server log-in, and making it simple and user-friendly.
Clear guidance for GPU driver installation and deployment eliminates the need for high learning costs.
Requires purchasing resources related to installation, with independent hardware scalability and driver installation.
Requires log-in through a jump server, making operations more complex.
Security
Full resource isolation among different users, ensuring data security.
Comprehensive security group and network ACL settings allow you to control inbound and outbound traffic to and from instances and subnets, and apply security filtering.
Seamless integration with cloud security, offering the same basic cloud security and high-defense services as CVM.
Shared resources among different users lead to a lack of data isolation.
Requires additional purchase of security protection services.
Costs
Provides the monthly subscription option, eliminating the need for significant capital investment in physical servers.
Keeps up with the latest GPU hardware updates, removing the hassle of hardware replacement.
Low server Ops costs, without the need for upfront hardware procurement and preparation, effectively reducing infrastructure investment.
High server investment and operation costs.
High power consumption, requiring hardware modifications and adaptations.
High IT Ops costs are required to ensure service stability.
Comparison between Cloud GPU Service and CPU CVM:
Dimension
GPU
CPU
Cores
Thousands of acceleration cores, such as dual M40 cards with up to 6,144 acceleration cores.
Dozens of cores
Features
1. Efficient and numerous arithmetic logic units (ALUs) enable parallel processing.
2. Multithreading achieves exceptionally high parallel throughput.
3. Simplified logic control.
1. Sophisticated logic control units
2. Powerful ALUs
3. Complex logic control
Applicable Scenario
Compute-intensive and easily parallelizable programs.
