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The emergence of Graphics Processing Units (GPUs) and the exponential computing power they unlock has been a defining moment for startups and enterprises alike.
GPUs provide impressive computing power to perform complex tasks involving technology such as ai, machine learning, and 3D rendering.
However, when it comes to harnessing this abundance of computing power, the tech world is at a crossroads in terms of what the ideal solution is. Should I build a dedicated GPU machine or use the GPU cloud?
This article delves into the heart of this debate, analyzing the cost implications, performance metrics, and scalability factors of each option.
GPUs (Graphic Processing Units) are computer chips designed to quickly render graphics and images by completing mathematical calculations almost instantly. Historically, GPUs were often associated with personal gaming computers, but they are also used in professional computing, and technological advances require additional computing power.
GPUs were initially developed to reduce the workload placed on the CPU by modern graphics-intensive applications by rendering 2D and 3D graphics using parallel processing, a method that involves multiple processors handling different parts of a single task.
In business, this methodology is effective at accelerating workloads and providing enough processing power to enable projects such as artificial intelligence (ai) and machine learning (ML) modeling.
GPU use cases
GPUs have evolved in recent years and have become much more programmable than their previous counterparts, allowing them to be used in a wide range of use cases, such as:
- Fast rendering of 2D and 3D graphics applications in real time, using software such as Blender and ZBrush
- Video editing and creating video content, especially pieces that are in 4k, 8k or have a high frame rate.
- Provides the graphical power to display video games on modern displays, including 4k.
- Accelerate machine learning models, from the basics image to jpg conversion to implement custom models with complete interfaces in a matter of minutes
- Share CPU workloads to deliver higher performance in a variety of applications
- Provide the computational resources to train deep neural networks.
- Mining cryptocurrencies like bitcoin and ethereum
Focusing on the development of neural networks, each network consists of nodes and each of them performs calculations as part of a larger analytical model.
GPUs can improve the performance of these models in a deep learning network through increased parallel processing, creating models that have greater fault tolerance. As a result, there are now numerous GPUs on the market that have been created specifically for deep learning projects. ai-models.html” rel=”noopener” target=”_blank”>like the recently announced H200.
Many companies, especially startups, choose to build their own GPU machines due to its cost-effectiveness, while still offering the same performance as a cloud GPU solution. However, this does not mean that a project of this type does not present challenges.
In this section, we’ll discuss the pros and cons of building a GPU machine, including expected costs and machine management, which can impact factors such as security and scalability.
Why build your own GPU machine?
The key benefit of building a local GPU machine is cost, but such a project is not always possible without significant in-house expertise. Ongoing maintenance and future modifications are also considerations that may make such a solution unfeasible. But, if such construction is within the capabilities of your team, or if you have found an outside vendor who can complete the project for you, the financial savings can be significant.
It is recommended to build a scalable GPU machine for deep learning projects, especially considering the rental costs of cloud GPU services such as Amazon EC2 Web Services, Google cloudeither Microsoft Azure. Although a managed service can be ideal for organizations looking to start their project as soon as possible.
Let’s consider the two main benefits of a local self-built GPU machine: cost and performance.
Costs
If an organization is developing a deep neural network with large data sets for ai and machine learning projects, operational costs can sometimes skyrocket. This can prevent developers from getting the intended results during model training and limit the scalability of the project. As a result, the financial implications can result in a reduced product or even a model that is not fit for purpose.
Building a GPU machine that is on-site and self-managed can help reduce costs considerably, giving developers and data engineers the resources they need for extensive iteration, testing, and experimentation.
However, this just scratches the surface when it comes to locally built and running GPU machines, especially for open source LLM. which are increasingly popular. With the advent of real UTIs, you may soon be seeing your friendly local dentist. run a couple of 4090 in the back room for things as insurance verificationprogramming, data crossing and much more.
Performance
Extensive deep learning and machine learning training models/algorithms are resource-intensive, meaning they need extremely high-performance processing capabilities. The same can be said for organizations that need to render high-quality video, and employees require multiple GPU-based systems or a next-generation GPU server.
Self-built systems with GPUs are recommended for production-scale data models and their training, and some GPUs can provide double precision, a feature that represents numbers using 64 bits, providing a wider range of values and better decimal precision. However, this functionality is only necessary for models that depend on very high precision. A recommended option for a double precision system is Nvidia’s Titan-based local GPU server.
Operations
Many organizations lack the experience and capabilities to manage on-premises servers and GPU machines. This is because an in-house IT team would need experts who are capable of configuring a GPU-based infrastructure to achieve the highest level of performance.
Furthermore, their lack of experience could lead to a lack of security, resulting in vulnerabilities that could be attacked by cybercriminals. The need to expand the system in the future may also present a challenge.
On-premise GPU machines offer clear advantages in terms of performance and cost-effectiveness, but only if organizations have the necessary in-house experts. That’s why many organizations choose to use GPU cloud services, such as Saturn Cloud, which is fully managed for simplicity and peace of mind.
Cloud GPU solutions make deep learning projects more accessible to a broader range of organizations and industries, and many systems can match the performance levels of homegrown GPU machines. The emergence of cloud GPU solutions is one of the main reasons why people are ai/best-ai-app-development-companies/” rel=”noopener” target=”_blank”>invest in ai development increasingly, especially open source models like Mistralwhose open source nature is tailor-made for ‘leasable vRAM’ and running LLM without relying on larger vendors such as OpenAI or Anthropic.
Costs
Depending on the needs of the organization or the model being trained, a cloud GPU solution could be more economical, as long as the hours needed each week are reasonable. For smaller, less data-intensive projects, there’s probably no need to invest in a pair of expensive H100s, as cloud GPU solutions are available on a contract basis, as well as in the form of various monthly plans, catering to enthusiasts in all their needs. way to the company.
Performance
There are a variety of cloud CPU options that can match the performance levels of a home GPU machine, providing optimally balanced processors, precise memory, a high-performance disk, and eight GPUs per instance to handle individual workloads. Of course, these solutions may come at a cost, but organizations can arrange hourly billing to ensure they only pay for what they use.
Operations
The key advantage of a cloud GPU over a GPU build is in its operations, with a team of expert engineers available to help with any issues and provide technical support. A local GPU machine or server must be managed internally or will need to be managed remotely by a third-party company, which incurs an additional cost.
With a GPU cloud service, any issues such as network breakdown, software updates, power outages, equipment failures, or insufficient disk space can be resolved quickly. In fact, with a fully managed solution, these issues are unlikely to occur as the GPU server will be configured optimally to avoid system overloads and crashes. This means IT teams can focus on core business needs.
The choice between building a GPU machine or using the GPU cloud depends on the use case, as large data-intensive projects require additional performance without incurring significant costs. In this scenario, a self-built system can offer the required amount of performance without high monthly costs.
Alternatively, for organizations that lack in-house expertise or do not require high-level performance, a cloud-managed GPU solution may be preferable, where the vendor takes care of machine management and maintenance.
Nahla Davies is a software developer and technology writer. Before dedicating her full-time job to technical writing, she managed, among other interesting things, to work as a lead programmer at an Inc. 5,000 experiential brand organization whose clients include Samsung, Time Warner, Netflix, and Sony.
