Finding the right foundation for your company’s advanced computing tasks is essential. Cloud-hosted servers offer a range of capabilities that can be tailored to address virtually any unique business requirements. Organizations performing computationally intensive tasks such as machine learning can benefit from the high-performance computing potential of dedicated GPU servers. GPU dedicated servers offer computational power for high-performance computing tasks not available in traditional CPU-based servers.

Traditional servers rely on the processing power of a central processing unit (CPU). A CPU is a general-purpose processor designed for computing tasks focused on sequential processing. Advanced and fast CPUs are exceptional at performing certain types of data processing tasks. CPUs are not as effective when asked to engage in the types of parallel processing tasks necessary to support advanced scientific research, machine learning, or complex video rendering.

GPU servers support technologies that require additional computational capabilities or parallel processing power. GPUs are essential in supporting emerging artificial intelligence and virtual reality solutions that promise to transform how we live, work, and spend our leisure time. Traditional computing solutions do not provide the power or functionality demanded by these new technologies.

Hosting dedicated GPU servers in the cloud offers companies of any size a chance to implement GPU servers to handle complex tasks beyond the capabilities of their CPU-based systems. An organization does not need to absorb the costs of implementing GPU servers in an onsite data center. We will look at the technical advantages of GPU dedicated servers and the business benefits of attaining their enhanced performance with a cloud hosting solution.

What Are GPU Dedicated Servers?

A dedicated GPU server is a powerful computing machine with one or more graphics processing units (GPUs). The GPUs act as the primary processing units for enhanced performance when handling certain complex tasks. Adding a graphics processing unit enables GPU servers to support high-performance workloads like video rendering, data analytics, machine learning, and artificial intelligence.

A dedicated server indicates that the server’s computing resources are provisioned exclusively for one client. A dedicated computing platform offers enhanced control and security over a shared hosting solution.

Benefits of GPU Servers

GPU servers offer several benefits over traditional CPU-based servers. These advantages make a GPU dedicated server the best choice for applications requiring high-performance computing capabilities.

Graphics processing units

The main distinguishing characteristic of GPU dedicated servers is that they contain at least one dedicated graphics processing unit to supplement their central processing units (CPUs). The GPUs provide additional computing resources making the servers more efficient at performing complex computational tasks such as training machine learning algorithms.

GPU cores

High-performance GPUs obtain their immense computational power from specialized GPU cores. GPU cores differ from those equipped in traditional CPU-based systems and can be deployed in large numbers to deliver parallel processing and high-performance computing capabilities. Advanced GPUs may utilize thousands of cores for enhanced performance metrics.

GPU cores can handle multiple tasks simultaneously and manage numerous threads to support concurrent operations. Cores are optimized to enhance performance on computationally intensive tasks such as video rendering or teaching machine learning models. Efficiency is promoted by sharing resources such as cache memory.

GPUs are typically designed using the following types of cores.

  • CUDA Cores: Compute Unified Device Architecture (CUDA) cores are specialized cores that provide high parallelism and memory bandwidth to support parallelized algorithms.
  • Tensor Cores: These specialized cores dynamically adapt calculations to accelerate throughput while maintaining accuracy and enhanced security to support AI and high-performance computing.
  • RT Cores: Ray tracing (RT) cores accelerate the mathematical calculations necessary for efficient ray tracing for realistic image rendering.
  • Sparsity Cores: These cores are specialized Tensor Cores that accelerate and optimize matrix computations to enable faster neural network processing.

Parallel processing capabilities

A major advantage of GPU servers is their parallel processing capabilities. A GPU server relies on several components besides specialized cores to deliver its immense computational power.

  • Video RAM (VRAM): VRAM is specialized memory that provides faster data transfer. GPUs also leverage cache memory to store frequently used data and improve processing speed.
  • Framebuffer: The GPU’s framebuffer streamlines rendering graphics by storing finished images before they are displayed. The framebuffer is essential for producing smooth graphics in games and virtual reality applications.
  • Advanced cooling systems: GPUs produce excessive heat and require additional measures to cool the processor. Lack of cooling negatively impacts the performance capabilities of GPU servers.

Usage Scenarios for GPU Dedicated Servers

Based on the benefits of GPU dedicated servers previously discussed, we can identify multiple usage scenarios where the use of GPU servers results in significant performance improvements. In many cases, traditional, CPU-based servers cannot provide the enhanced performance and advanced computational resources necessary to efficiently address these use cases.

Video processing

GPUs are designed to streamline video processing tasks such as graphics rendering. Leveraging the power of multiple, specialized cores provides enhanced speed when a GPU server performs functions such as displaying graphics. High-speed video rendering delivers smoother displays essential for effective virtual reality presentations. GPUs are essential for addressing the advanced computing requirements of scientific research and modeling complex simulations. They are also essential for delivering smooth and consistent animations required by gaming platforms.

Analyzing large datasets

The parallel processing capabilities of GPU servers make them efficient at analyzing large datasets. Data analytics and big data require the real-time processing of large volumes of information. Big data applications like fraud detection, financial analysis, and customer sentiment analysis rely on the ability to efficiently process large datasets. GPU servers also speed up the production of visualizations that make the results of data analysis more accessible to a larger audience.

Training machine learning models

Machine learning (ML) is a discipline within artificial intelligence that has become a cornerstone of many emerging applications and technical solutions. Machine learning applications automate processes for productive gains throughout an organization. The effectiveness of ML tools is directly influenced by the training they receive.

ML models can be trained using several different methods. Supervised learning is done by example. An operator presents the model with datasets containing known inputs and outputs. The model is trained to observe and predict outcomes when new data is presented.

Unsupervised learning trains a model without the intervention of a human operator. Reinforcement learning is another option that teaches the model through trial and error.

The parallel processing capabilities of GPU dedicated servers make them an excellent choice for training ML models. The computational power of GPU servers is much more efficient at ML training tasks than traditional servers.

Deep learning

Deep learning requires the efficient data analysis of even larger volumes of information than machine learning. Large language models (LLMs) are usually used when training deep learning models. Artificial neural networks are the building blocks of deep learning systems. The processing power of a GPU server is essential to successfully train deep learning applications.

Deep learning systems have many business and societal applications. Popular programs like ChatGPT leverage natural language processing (NLP) to interact seamlessly with users. The technology behind smart cities is based on deep learning models that can perform tasks like managing traffic or conducting surveillance.

Complex mathematical calculations

Organizations performing complex mathematical calculations across any field can benefit from the enhanced processing power of GPU servers. GPU dedicated servers work more efficiently with floating-point mathematics and provide substantially improved performance compared to CPU-based systems. Many organizations leverage the mathematical capabilities of GPUs.

Scientific research and simulations

GPU servers are instrumental in conducting scientific research and producing simulations to study complex systems and reactions. The following examples demonstrate the types of research facilitated by GPU-dedicated systems.

  • Bioinformatics: Large datasets must be processed for DNA sequencing and other biological simulations.
  • Climate modeling: The complex mathematical calculations required by weather forecasting and climate modeling can be accelerated by employing GPU servers.
  • Physics: GPU servers’ parallel processing ability facilitates presenting simulations of complex physical processes like quantum mechanics and fluid dynamics.

Computer vision

Computer vision has many applications in multiple disciplines. Real-time and accurate computer vision is imperative for self-driving vehicles and autonomous robots. Facial recognition software also relies on computer vision to identify specific individuals.

Graphics processing units provide the computing resources necessary to support effective computer vision. GPU dedicated servers can handle the intensive graphical processing required to artificially replicate human vision.

Medical imagery and healthcare

GPU servers are often deployed for real-time medical imaging and MRI processing to provide fast and accurate diagnosis. GPU servers are also widely used in the pharmaceutical industry to run simulations related to discovering new drugs and treatment options. GPU speed and accuracy are essential factors in developing vaccines to address viruses that mutate quickly.

Crypto-mining and transaction validation

Complex mathematical calculations are required for activities like crypto-mining and validating blockchain transactions. GPU servers’ high-performance computing capabilities support these processor-intensive processes. GPU dedicated servers perform the complex calculations and data analysis required for these activities more efficiently than CPU-based servers.

Business Advantages of GPU Dedicated Hosting

Companies benefitting from parallel computing capabilities or performing intensive computational tasks can leverage Atlantic Net’s GPU dedicated server hosting offerings. Businesses can enjoy multiple advantages by utilizing GPU servers from a cloud service provider (CSP).

Cost savings

Using hosted GPU dedicated servers offers companies a cost-effective solution to obtaining the processing power for complex workloads without a significant upfront investment. GPU servers cost more than traditional servers and may not be suitable for some of an organization’s more common computing tasks. GPU use is typically reserved for the usage scenarios discussed above.

Eliminating the need to purchase specialized hardware results in substantial savings and allows smaller companies with limited budgets to access advanced computational resources. This levels the playing field in today’s competitive business landscape and fosters the development of new and innovative products and services.

Scalability and flexibility

Obtaining the computational power of GPU servers from a CSP gives customers a scalable and flexible platform to address business requirements and objectives. As business needs evolve, the CSP can provide any necessary additional resources without additional capital expenditures by the client.

Scaling up and down to align with customer trends or demand fluctuations can save significant financial resources while optimizing performance. Cloud hosting eliminates concerns regarding the over or under-provisioning computing resources through dynamic scalability.

Configuration control

A dedicated GPU server gives the client more control over server configuration. This allows them to tailor the GPU server to address unique business requirements more efficiently. No concessions need to be made to meet the needs of other clients.

For example, parameters such as memory bandwidth can be configured to enhance system performance for specific usage scenarios. Clients can customize a dedicated server without being concerned about how it will affect other tenants of a shared server. This allows for optimization that typically results in improved productivity.

Enhanced security

A dedicated hosting solution provides resources to a single client. Isolating a company’s data processing furnishes improved security by eliminating the possibility of the system being impacted by threat actors exploiting the ineffective security of other server tenants. Clients can implement cybersecurity measures to protect their computing environment, sensitive data, and applications.

Reliable resource availability

GPU dedicated servers provide more consistent overall system performance compared to shared servers. Sharing a server can result in fluctuating resource availability as the demands of other tenants are addressed. Utilizing a dedicated GPU server guarantees that resources are always available to efficiently process data and run applications.

Host Your GPU Dedicated Server with Atlantic Net

Atlantic.net offers customers Next-Gen Dedicated GPU Servers accelerated by NVIDIA to handle the parallel processing and complex computations required by the most demanding business or scientific application. We offer systems built around two different NVIDIA GPUs to address your unique computing requirements.

  • NVIDIA L40S GPU: The NVIDIA L40S GPU provides customers with multi-workload performance leveraging powerful AI computing ability with best-in-class graphics and media acceleration. The GPU is designed to address the needs of next-generation workloads like medical imaging, training generative AI with LLMs, and graphics rendering.
  • NVIDIA H100 NVL: This platform is highly optimized for LLM inferences. It combines high compute density and memory bandwidth with energy efficiency provided by its unique NKLink architecture. The GPU delivers exceptional acceleration, allowing it to power complex simulations, artificial intelligence, and high-performance computing applications.

Talk to Atlantic.net’s GPU dedicated hosting experts and give your machine learning and complex, high-performance computing tasks the proper foundation.