It’s critical to select the correct server for the workload you’ll be running, especially if you’re doing anything related to AI/ML, data processing, or rendering. A traditional server may work for simpler business workloads, but more intensive tasks need a different approach. Sometimes, a GPU server can solve that problem.
If you’ve recently started looking into GPUs and are trying to figure out what is GPU and whether your business needs a GPU server, it’s always better to understand the basics and make the right investment for your business. Just looking at the price or model of a GPU can lead to suboptimal performance or costly decisions.
In this article, we will answer some of the basic questions around GPU servers, including what a GPU is and how a GPU server works, which industries and use cases benefit most from GPU servers, and how to select the best GPU server for your workload and budget.
Table of Contents
What is a GPU?
A GPU is a Graphics Processing Unit. It’s a processor designed to efficiently perform thousands, or even millions, of calculations in parallel. GPUs were originally created for graphics processing, but because of their nature of being good at calculating tens of thousands of things at the same time, they are good for many other workloads like artificial intelligence, and data processing.
When someone asks you what a GPU is, you can think of it as a processor good at doing lots of things at the same time, like a box of Trix.
A GPU can divide many of these tasks into smaller operations and process them in parallel.
What Is the Difference Between a CPU and a GPU?
A CPU (Central Processing Unit) and a GPU serve different but complementary purposes.
| Feature | CPU | GPU |
| Main role | Handles general-purpose computing and system tasks | Handles highly parallel calculations |
| Processing approach | Typically optimized for a smaller number of complex tasks | Typically optimized for many simultaneous operations |
| Common uses | Operating systems, databases, business applications | AI training, graphics rendering, simulations |
| Core strength | Flexible processing and task coordination | Parallel processing throughput |
A CPU is essential for running the server’s operating system, managing applications, and coordinating tasks. A GPU accelerates workloads that can benefit from parallel computation.
Neither processor replaces the other. A well-configured GPU server needs a suitable CPU, sufficient memory, fast storage, and reliable networking alongside its GPU hardware.
What Is a GPU Used For?
Applications for a GPU include:
- Artificial intelligence: Machine learning, image/video recognition, and prediction
- 3D rendering: Realistic images, animations and effects
- Video production: Speed up tasks including encoding and editing videos
- Scientific research: Computational modeling and numerical simulations
- Data analytics: Speeding up compatible data-processing workloads.
- Gaming and visualization: Supporting graphics-intensive applications and virtual environments.
A note on GPU servers: These machines typically contain multiple GPUs which can be interconnected and used in conjunction to work on larger data sets.
Performance is related to software, GPU architecture, memory, and workload. Not all applications benefit from using a GPU. You should identify your requirements to evaluate if you really need to buy a server.
What Is a GPU Server?
A GPU server is a computing environment that is powered by one or more GPUs. These servers speed up workloads and are especially useful when heavy computation is required.
GPU servers can be offered as a physical dedicated machine or a hosted or virtual machine in the cloud. The best option depends on requirements, usage, and cost.
How Does a GPU Server Work?
- A GPU server works by partitioning work between the GPU and the CPU.
- The CPU works to run the operating system, manages application logic and general computation.
- The application, based on requirements, sends computational tasks to the GPU.
- The GPU works to perform multiple operations in parallel.
The CPU and the application retrieve the results and continue with the remaining work.
For example, consider an AI development team that is building an application to recognize images. The CPU is responsible for data preparation and general computation in the application. The GPU is responsible for performing computations to train the image recognition model. A GPU can speed up model training compared to a CPU, although results vary based on the software, model, and hardware configuration.
A single GPU server can be expanded to include multiple GPUs. Adding more GPUs can increase the processing capacity for certain workloads. However, the application has to be able to partition workloads and make use of multiple devices to gain any benefit from using multiple devices. For some applications adding more GPUs can actually slow things down.
Why Do Businesses Need GPU Servers?
As businesses incorporate more AI, automation, visualization, and data-centric applications, computational demands increase. GPU servers can help businesses better fulfill demanding workloads when those workloads can take advantage of GPU acceleration.
Some of the main benefits include:
1. Faster AI/ML Workloads
Many mathematical operations in Machine Learning can number in the millions and billions. A GPU server can help speed up these operations and allow developers to experiment with larger models.
For example, a startup that creates an AI document analysis tool would likely want to use a GPU server to train the model. The server configuration would need to match the memory requirements of the model, the expected concurrent users, and response time requirements.
2. Faster Visual Production
Production of 3D animated scenes, architecture, and product visualization can all be very data-heavy and therefore benefit from GPU acceleration.
As with other workloads, check the software you plan to use and ensure the hardware you’re considering is supported by your software and rendering engine.
3. Data-Centric Computation
Some analytics, simulations and scientific workloads can benefit from GPU Acceleration. Some businesses that deal with large data sets may want to use GPU-accelerated software to analyze and work with the data.
A workload that relies on disk access, database queries, or other serial CPU processes will likely not benefit from a GPU.
4. Flexible Infrastructure Choices
Different providers will give you different options for deploying GPU resources, from allowing you to rent GPU resources, providing you with a dedicated GPU server, or allowing you to deploy a cloud instance with a dedicated GPU. Renting GPU resources can help alleviate the need for a large upfront cost for hardware, while a dedicated GPU server provides you with a consistent and reliable resource.
5. Ability to Support Growing Workloads
A GPU server can provide the resources needed to support additional workloads, such as larger AI models, more rendering jobs, and/or more users. A server that provides flexibility to grow helps reduce the cost for overprovisioning.
Choosing the Right GPU Server for Your Workload
The best GPU server is the one that meets your requirements for a workload, is reliable, and is within your budget.
Scientific computing: are your frameworks compatible? What numerical precision is required?
Step 1: Identify Your Main Workload
Start by deciding exactly what you will run. Different applications need different hardware configurations.
- AI model training: Look for strong GPU compute performance, sufficient GPU memory, and support for multi-GPU workloads if required.
- AI inference: Consider model size, response time, throughput, and the number of simultaneous requests.
- 3D rendering: Check compatibility with your rendering software, supported graphics APIs, and available GPU memory.
- Video processing: Confirm support for the codecs, encoding features, and software you use.
- Scientific computing: Verify compatibility with your frameworks and required numerical precision.
- General hosting: If your applications are not GPU-intensive, a conventional CPU server may be more cost-effective.
Write down your application name, workload size, expected usage, and performance target before comparing servers.
Step 2: Select the Right GPU Model
GPUs differ in processing capability, memory capacity, power consumption, and supported features. Some GPUs are designed for consumer use, while others are designed for professional use including visualization, AI, and data center.
When considering a GPU, evaluate the following:
- Are the frameworks you need supported on the GPU?
- What is the capacity of the GPU memory?
- What is the memory bandwidth?
- How well does the GPU perform for your workload?
- Is enough power and cooling support available?
- Are drivers and support available for the GPU?
When considering GPUs, don’t just consider model numbers. Newer and more expensive GPUs may not be a better choice for your application.
Step 3: GPU Memory (VRAM)
GPU memory (or VRAM) plays an important role in the performance of many GPU workloads. Some of the data that the GPU needs to process such as model parameters, intermediate results, textures, and other work data is stored in GPU memory.
For AI, if GPU memory is not sufficient, models may not be able to fit on the device, resulting in the use of techniques such as smaller batches, quantization, and/or model partitioning.
For 3D rendering, higher memory (or VRAM) is required for more complex scenes, textures, and higher resolution.
Prior to renting or buying a server, calculate the amount of memory required by your application, and leave some headroom for larger workloads.
Step 4: Evaluate CPU and System RAM
A GPU server still needs a good CPU and system memory. The CPU can process data and organize applications, and can help alleviate some of the burden from the GPU.
If the CPU and system memory are insufficient, the GPU may not be able to maintain full utilization and provide return on investment.
For workloads with data preparation and processing or dealing with several applications, it can be beneficial to evaluate a CPU with a good core count, large memory capacity, and high memory bandwidth.
Step 5: Choose Fast and Sufficient Storage
For applications that process large datasets, and especially ones that involve training or validating neural networks, having fast storage can help process datasets and store training and checkpoint files.
If high-resolution media (e.g. videos) are being processed, fast storage can help as well.
NVMe SSDs can be used to provide high-performance storage; however, the capacity of the storage and storage design should align with project requirements.
For a project, consider the following questions:
- How large is the dataset being processed?
- How large are the training and checkpoint files?
- How large are the media files being processed?
- Is the storage design adequate for future growth?
- Does the storage provider offer backup and recovery services?
Step 6: Compare Network Speed and Connectivity
When datasets are remote and applications serve users that are geographically dispersed, network speed and performance become even more important, especially if several servers are exchanging large amounts of data (i.e. for distributed AI training).
How close are your users to the data center that hosts your application? Choose a server location that gives you lower latency for your users.
Step 7: Review Pricing and Total Cost
There can be many reasons for variations in prices of different GPU servers. For example, variations can be because of different GPUs, memory, storage, networks, and CPU configurations. Variations can also be due to different hosting arrangements. When examining prices, be sure to evaluate the total cost of ownership and not just the advertised rates (i.e. hourly or monthly rates).
Check whether the price includes:
- Operating System
- Software
- Licensing
- Setup and Configuration
- Data Transfer
- Backup and Monitoring
- Technical Support
- Additional Storage
- Taxes and Other Fees
If your workload will run continuously, compare long-term rental costs with costs for dedicated hardware. For short-term projects, continuous availability of GPU servers can be very useful.
Step 8: Security, Reliability, and Support
When evaluating reliability of services, look for the following features:
- Remote Access
- Network Security
- Data Monitoring and Troubleshooting
- Backup of Important Data
- Uptime and Commitment
- Hardware Replacement
- Service Terms and Procedures
For business critical workloads, check what happens when a GPU or another component fails. Also, check what services, if any, the hosting provider offers to assist with installing CUDA or other required software, and configuring the OS.
Step 9: Test Before Committing
If you’re able to, benchmark your actual application on the proposed GPU server. You should test to see if the configuration meets your performance goals.
Look at metrics like processing time, GPU memory, utilization, throughput, and cost per task. For AI inference, look at response time with realistic request volume. For model training, look at training time and stability with representative workloads.
A small benchmark is typically more helpful than relying on published specifications to make a hardware choice.
GPU Server vs. CPU Server: Which Should You Choose?
The choice depends on your application’s processing needs.
| Requirement | CPU Server | GPU Server |
| Business websites and standard applications | Often suitable | May be unnecessary |
| Traditional database workloads | Often suitable | Useful only for compatible workloads |
| AI model training | Can work, but may be slower for GPU-optimized tasks | Often suitable |
| Deep learning inference | Depends on model and traffic | Useful for supported models and performance targets |
| 3D rendering | Suitable for some CPU-based rendering | Suitable for GPU-accelerated rendering |
| Scientific simulations | Depends on the software | Useful when the workload supports GPU acceleration |
For example, a small business hosting a company website may not need GPU resources. An AI company processing thousands of image requests, on the other hand, may benefit from a GPU server if its model and software are optimized for GPU execution.
The key is to match the hardware to the task instead of paying for computing resources that remain unused.
Mistakes to Avoid When Selecting a GPU Server
Looking to purchase a GPU server? Watch out for the following mistakes:
- Making your selection solely based on price. At that price, is it really a GPU server? It could be missing critical GPU memory or system resources.
- Ignoring software compatibility. Does your framework, driver and app support the selected GPU?
- Not considering cooling and power. Do the GPUs have adequate cooling and power?
- Thinking more GPUs means better performance. Is the software capable of utilizing multiple GPUs?
- Not looking at the storage and networking. Is the networking or storage subsystem a bottleneck?
- Forgetting to look at long term costs. What about license, support, backups and data transfer costs?
- Skipping performance testing. Test multiple real world workloads.
Checking these details can help you not overspend and choose a config that is actually usable.
Frequently Asked Questions (FAQs)
1. What is a GPU in simple words?
A GPU (Graphics Processing Unit) is a processor that is designed to perform many calculations at the same time for graphics and computing tasks.
2. What does GPU stand for?
GPU stands for Graphics Processing Unit.
3. What is a GPU server used for?
In general, a GPU server is used for any computational workload that can be accelerated by one or more GPUs. Some examples include but are not limited to AI and machine learning, 3D rendering, visual effects, and data center applications.
4. How much GPU memory do I need for an AI server?
This is highly dependent on your model, batch size, software, and dataset. Generally speaking, larger and more complex models tend to require more GPU memory. The best way to gauge the requirements is to look at the documentation for your framework and/or model and test it on the target hardware.
5. Is it better to rent or buy a GPU server?
It is commonly beneficial to rent a GPU server if you need resources only for a short project or if you don’t want to make the upfront capital expense for the server. It may be beneficial to purchase or lease GPU servers if you have consistently heavy workloads and factors such as utilization, maintenance costs, and your organization’s constraints support that decision.
Conclusion: Select a GPU Server that Works for You
A GPU can certainly speed up a workload, but it is important to understand how the entire system works. This includes looking at GPU memory, CPU, RAM, storage, networking, and how the software is configured.
Prior to selecting a GPU server, define your workload, analyze your resource requirements, compare total costs, and assess performance where possible.
When you’ve completed this research, you will likely be able to identify the right GPU server for your needs, and not pay for excess resources.
When you’re ready to move forward, you should examine the available options for GPU server hosting, look at the hardware specifications, and reach out to potential providers to describe your workload and requirements and see what they recommend.
