GPU is becoming more and more popular nowadays. Today we are going to talk about GPU cloud servers.
Why is GPU so popular? Why is CPU being left behind?
In simple terms, GPU and CPU are two different types of computer processors.
We know that any processor is composed of three parts: arithmetic logic unit (ALU), control unit and cache. The CPU is better at handling one task at a time, while the GPU can handle multiple tasks simultaneously. Just like some people are good at performing tasks one by one in sequence, while others can handle multiple tasks at the same time.
Let me use an analogy to explain the difference between the two in a simple way. The CPU is like a racing motorcycle, while the GPU is like a bus. If the task of both is to send one person from position A to position B, then the CPU (motorcycle) will definitely arrive faster. However, if 100 people are sent from position A to position B, then the GPU (bus) can transport more people at a time, so it will take less time to transport 100 people.
Simply put, the CPU can execute tasks faster at a single time, but the GPU advantage becomes more significant when a large number of repetitive workloads are required (such as matrix operations: (A*B)*C). Therefore, although the CPU has a faster single delivery time, the GPU advantage is more significant when handling image processing, animation rendering, deep learning and other workloads that require a lot of repetitive work.
To sum up, the CPU is good at processing a single task. GPUs are very good at parallel processing, which also makes them ideal for computationally intensive applications. Nowadays, GPUs are increasingly used for workloads such as artificial intelligence (AI) and machine learning (ML).
What is a GPU Cloud Server?
As computing demands grow, especially for applications requiring high-definition vision and complex operations such as deep learning and graphics rendering, the need for more powerful resources has driven advances in GPU technology.
Nowadays, many services of businesses and individuals cannot be done without GPU computing. GPUs have even become a rare commodity, and the internal management of these GPU hardware is costly and complicated.
As GPU technology rapidly advances, cloud-based GPUs have become an attractive alternative, providing access to the latest GPU usage without maintenance challenges or high upfront costs.
GPU Cloud Server
GPU cloud server is a fast, stable and elastic cloud computing service based on GPU, which is mainly used in scenarios such as deep learning training/inference, graphics and image processing, and scientific computing. GPU cloud servers provide the same convenient and fast management methods as standard CVM cloud servers.
In addition, GPU cloud servers can effectively relieve users’ computing pressure and improve business processing efficiency and competitiveness through their powerful computing performance that can quickly process massive amounts of data.
To put it in plain words: GPU cloud server is a cloud service that can provide GPU computing. In the past, you had to buy the GPU yourself, build the house yourself, and take care of whether the house leaks or the sewer is unobstructed… With a GPU cloud server, you don’t have to worry about these anymore. There are dedicated housekeepers to help you take care of it, and you can use the computing power of the GPU to serve your business in a stable manner.
Moreover, if you are unsatisfied, you can just leave at any time. This is the good side of using a GPU cloud server.
Why Use GPU Cloud Server?
Currently, local GPUs are manufactured by companies such as NVIDIA, AMD, and Intel, which design and produce hardware that is installed directly into personal and enterprise systems in computer rooms.
Service providers that can provide GPU cloud servers include Alibaba Cloud, AWS, Google Cloud, Microsoft Azure, etc., allowing customers to rent GPUs on demand for AI/ML tasks.
Let’s take a closer look at how they differ in terms of the following factors:
| Parameter | Physical GPU | GPU Cloud Server |
| Infrastructure | Requires physical installation and management of GPU hardware | Hosted and managed by cloud service providers, no physical hardware setup required |
| Scalability | Limited scalability Requires purchase and upgrade of physical hardware | Easily expandable based on demand |
| Cost | High initial investment in hardware, plus maintenance costs | Flexible billing methods, no upfront hardware costs Pay-as-you-go pricing based on usage |
| Performance Control | Full control over performance tuning and system optimization | Optimized based on provider’s infrastructure, with some limitations on customization |
| Upgrade | Requires manual upgrade or replacement of physical hardware | Automatic hardware updates from cloud providers |
| Maintenance | The user is responsible for the maintenance of the hardware and cooling system | No maintenance required by users, managed by the provider |
| Accessibility | Limited to local use (unless accessed via remote desktop software or a virtual private network) | It can be accessed anywhere as long as there is an Internet connection, and multiple users can share resources |
| Setup time | Time-consuming setup involving physical installation and configuration | Quick setup No physical components to install |
| Data Privacy/Security | Provides complete control over data storage and security Additional measures are required to ensure physical security and encryption | Reliance on the cloud provider’s security protocols Ensured compliance with data regulations and encryption |
At present, although some users still choose to have local GPUs, the popularity of GPU cloud servers has been growing. This is because on-premises GPUs typically require upfront costs and time for custom installation, management, maintenance, and eventual upgrades.
In contrast, GPU instances provided by cloud platforms only require users to use the services at an affordable price without these technical operations. These platforms provide all the services required to perform computations using GPUs and take care of managing the GPU infrastructure. Additionally, the responsibility of expensive upgrades is not left to the customer, who can switch between machine types as new machines become available without any additional cost.
This eliminates the technical processes required by self-manage local GPUs and allows users to focus on their business expertise, streamlining business operations and increasing productivity.
Moreover, using GPU cloud servers also saves time and is often more cost-effective than investing in and maintaining on-site infrastructure. This can benefit startups by converting the capital expenditure required to build and manage such computing resources into the operating cost of using GPU cloud servers, lowering the threshold for building deep learning infrastructure.
Of course, cloud platforms also offer other functions such as data migration, accessibility, integration with ML frameworks, databases, languages (such as Python, R, or Java), storage, security, upgrades, scalability, collaboration, control, and support for worry-free and efficient computing.
According to forecasts from professional organizations, the market size of GPU cloud servers will grow rapidly, from US$3.16 billion in 2023 to US$25.53 billion in 2030. This shift highlights the market’s gradual recognition of GPU cloud servers.
What are the Application Scenarios of GPU Cloud?
Having listed so many benefits of GPU cloud, what application scenarios are suitable for choosing GPU cloud server?
- Data Scientists and Machine Learning Engineers
If you are a data scientist or machine learning engineer, GPU cloud computing is your best friend. Imagine trying to train a deep learning model with a huge dataset. Without a GPU, this might take a long time. But with a GPU, you can process all this data in record time. It’s like having a super engine for your data needs.
Additionally, GPUs are well suited for running complex algorithms and simulations. Therefore, if you want to speed up model training or understand big data faster, GPU cloud servers will be a game changer.
- AI and Deep Learning Researcher
Researchers working in the field of AI and deep learning know how demanding their tasks are. Training neural networks and running simulations typically require a lot of computing power. This is where GPU cloud servers come into play. With the massive parallel processing power of deep learning GPU cloud servers, you can quickly obtain research results.
Imagine being able to run multiple experiments simultaneously without being bogged down. It’s like having a lab full of powerful machines, but without all the physical hardware. That way, you can focus on the exciting part—discovering new insights and innovations.
- Game Developer
For game developers, GPU cloud servers can make your dreams come true. Developing and testing graphics-intensive games can be very demanding on your hardware. The GPU does all the heavy lifting when it comes to rendering high-quality graphics and running complex game physics.
You are no longer limited by local hardware and can take advantage of GPU cloud servers to develop games with stunning visuals and smooth performance. It’s like having a state-of-the-art graphics that can be shared by every developer on your team.
- Video Editor and Animator
Video editors and animators work with high-resolution footage and complex effects. Rendering these can take a long time on a standard CPU. GPU cloud servers can significantly reduce rendering time through their powerful parallel processing capabilities.
Imagine that it turbo-charges your video production, so that you can render and process video faster, enabling you to get the final product to your clients or viewers faster. It’s a real productivity booster in the world of video editing and animation.
- Financial Analyst
Financial analysts who deal with real-time trading, risk management, and complex financial models will find GPU cloud servers very useful. GPUs can perform massive calculations quickly, which is critical for real-time analysis and decision-making.
Instead of waiting for models to calculate, which takes a long time, you can get faster results and make timely decisions. It’s like having a super calculator that helps you stay ahead in the fast-paced world of finance.
- Engineers and scientists in the field of bioinformatics
In fields such as bioinformatics, engineers and scientists often work with huge data sets and perform complex simulations. GPU cloud servers provide the power needed to analyze genetic data, run simulations, and efficiently process large amounts of information.
With GPUs, you can accelerate your research and gain insights faster. It’s like having a high-performance workstation, but without investing in expensive hardware. This means more time discovering and less time waiting for computations.
It can be said that the integration of GPUs into cloud computing represents a transformation for many industries, providing customers with unparalleled performance and flexibility. By leveraging GPUs in the cloud, businesses can accelerate data processing capabilities, from handling complex calculations and simulations to powering machine learning models and artificial intelligence applications. The ability to access high-performance GPUs on demand enables companies to efficiently scale resources without large upfront hardware investments.
Additionally, cloud-based GPUs offer convenience and accessibility that traditional on-site solutions often lack. With the cloud provider managing the infrastructure, users benefit from reduced maintenance costs and the freedom to focus on core projects rather than dealing with hardware issues. This on-demand access ensures that businesses can quickly adjust computing power as needed, facilitating a more agile and cost-effective approach to processing-intensive tasks.
How to Choose a Cloud GPU Service Platform?
Nowadays, there are many manufacturers that can provide GPU cloud servers, including Alibaba Cloud, Huawei Cloud at home, AWS, Google Cloud and Azure at abroad and other hyper-scale providers, all of which can provide customers with scalable, high-performance GPU solutions for application scenarios such as machine learning, AI and data analysis.
Moreover, there are some providers like EasyStack and QingCloud that focus on personalized solutions, dedicated support, and generally provide cost-effective pricing for developers, data scientists, and fast-growing enterprises.
So for GPU users, how can they distinguish the differences between these different cloud GPU server providers? How to find the best GPU cloud solution? We will look at this from the following aspects:
- Performance requirements
For many customers, GPU performance is the primary concern. When choosing a GPU vendor, you should first focus on the computing power that the GPU can provide. Besides, understanding the different GPU models, architectures, and performance capabilities will help you choose a GPU that perfectly aligns with your specific project needs, optimizes efficiency, and drives desired results.
For example, pay attention to the GPU’s clock speed and the number of CUDA cores or Tensor cores, as these elements directly affect the overall computational speed. Check VRAM capacity, as higher VRAM may be effective in handling storage of large datasets and complex models in memory-intensive tasks such as 3D rendering, video editing, and high-resolution image processing. Evaluate the GPU’s architecture and consider that newer architectures, such as NVIDIA’s Hopper, offer better performance and efficiency than their predecessors.
- Scalability
Scalability directly impacts how effectively the infrastructure can handle growing data volumes and increasingly complex model training processes. Choose a GPU cloud provider that supports elastic scaling so that GPU instances can be easily added or removed based on demand. For example, during periods of high demand, large language models used to train text generation or sentiment analysis can take advantage of scalability to process large amounts of text data in parallel, reducing training time while ensuring model accuracy as datasets grow.
- Cost Assessment
When choosing a GPU cloud server for your project, evaluate cost factors related to billing, GPU models, storage, and data transfer to avoid budget overruns.
Decide between pricing models, such as On-Demand vs. Reserved Instances. Most providers offer on-demand pricing because of its flexibility, where you are billed by the second or hour. While this model is well suited for workloads with fluctuating demand, reserved instances are better suited for long-term GPU usage, such as ongoing deep learning model training or real-time recommendation systems, where consistent GPU power is critical.
And the GPU type affects the cost, as high-performance GPUs are priced higher than entry-level options. While high-performance GPUs can speed up processing time, they can result in increased costs if the task doesn’t require it. For example, when you’re building an AI side project or an early prototype, you can opt for a more affordable option like the NVIDIA T4, allowing you to maintain performance without overspending. For larger, more complex projects, such as processing massive data sets in areas such as autonomous driving or climate modeling, which require faster processing and more compute power, invest in high-end GPUs such as the NVIDIA H100 to ensure your workloads run efficiently.
Additionally, data must be stored, accessed, and processed efficiently to maximize the performance of cloud projects. When exploring different storage options, such as block storage vs. object storage – consider choosing object storage when dealing with unstructured data, such as images, videos, or large data sets (without a predefined structure). Block storage is ideal if you are building a “speed-critical” application that requires low-latency access, such as a database or financial trading application.
Be aware of data transfer charges, especially egress costs, which can add up when moving large data sets around the cloud. While some providers may offer free data uploads (ingress), they may charge for downloads (egress), the fee of which can add up if you frequently retrieve results or transfer data across regions.
- Integrate with existing infrastructure
Make sure your cloud provider’s GPUs integrate with your current setup to avoid compatibility issues or outages. Start by confirming that the cloud provider supports the frameworks and libraries you already use, such as TensorFlow or PyTorch, and integrates smoothly with your storage and network configuration. Check whether the cloud provider’s infrastructure can handle the increased computing load when processing real-time data without latency issues.
Verify that your orchestration tools and operating system are compatible with the GPU, and run small-scale tests to catch any integration issues, such as compatibility issues with drivers or network configuration mismatches before scaling up. While taking these proactive steps will improve your overall workflow, being aware of potential vendor lock-in and evaluating cloud providers’ solutions may limit your flexibility and complicate future cloud migrations or multi-cloud strategies.
- Network and data transfer speeds
To ensure smooth and efficient performance, focus on how fast data moves between storage, compute instances, and GPUs. High bandwidth ensures data moves quickly between storage and the GPU, reducing any delays in processing. Low latency is critical for real-time tasks, such as gaming or live streaming, where even slight delays can affect performance. Fast data transfer speeds help avoid bottlenecks, ensuring your GPU gets the data it needs to keep tasks running smoothly. Additionally, a robust internal network for distributed workloads allows your instances to communicate effectively and improves overall performance.
- Support and reliability
Choose a cloud provider that offers solid support plans and strong uptime guarantees. For example, if you’re running critical applications like AI-driven diagnostics in healthcare, you’ll want a provider that ensures minimal downtime and responsive customer support. A trusted cloud provider ensures you meet deadlines and prevents costly setbacks such as project overruns, missed opportunities, or operational disruptions.
By providing consistent uptime and timely technical assistance, you can keep projects on track, avoid financial penalties, and maximize your cloud ROI by keeping projects on schedule and within budget.